
# Numerical Libraries
import numpy as np
import pandas as pd
import scipy
# Standard
import datetime, time, math, random
from itertools import permutations, combinations
from datetime import datetime, timedelta, date
import pandas_datareader.data as web
from matplotlib.dates import DateFormatter
import scipy.stats as stats
# Data Import Libraries
import yfinance as yf
import pandas_datareader as pdr
# Supress Warnings
import warnings
warnings.filterwarnings('ignore')
# Plots
import seaborn as sns
import networkx as nx
from pylab import plt, mpl
from pandas.plotting import scatter_matrix
from scipy.stats import kurtosis,norm,genpareto


# Function to get closing price from Yahoo. Input tickers as in Yahoo URL and Start/End Date YYYY-MM-DD
def GetData(T, Start, End):
    '''
    T: List of Stock indices, as in Yahoo URL for Download e.g ^GSPC for S&P 500
    Start: Date in YYYY-MM-DD
    End: Date in YYYY-MM-DD
    '''
    Close_ser = []
    t1 = int(pd.to_datetime(Start).timestamp())
    t2 = int(pd.to_datetime(End).timestamp())
    for i in T:
        URL = 'https://query1.finance.yahoo.com/v7/finance/download/'+i + '?period1=' + str(int(t1)) + '&period2='+ str(int(t2)) + '&interval=1d&events=history&includeAdjustedClose=true'
        Fin_DF = pd.read_csv(URL, header = 0, encoding = 'utf-8')
        Fin_DT = pd.to_datetime(Fin_DF['Date'])
        Fin_DF.insert(1,'DateTime', Fin_DT, allow_duplicates = False)
        Fin_DF = Fin_DF.drop(['Date'], axis=1).set_index('DateTime')
        Close_ser.append(Fin_DF)
    return Close_ser


def GetAdjCloseQuotes(T, Start, End):
    '''
    T: List of Stock indices, as in Yahoo URL for Download e.g ^GSPC for S&P 500
    Start: Date in YYYY-MM-DD
    End: Date in YYYY-MM-DD
    '''
    P = GetData(T, Start, End)
    Indices_DF = []
    for i in range(len(P)):
        Indices_DF.append(pd.DataFrame(P[i]['Adj Close'].values, index = P[i].index, columns = [T[i]]))
    return Indices_DF[0].join(Indices_DF[1:], how = 'inner')


Tickers = [ '^FTSE', '^GSPC', '^HSI','^NSEI','GLD','GOLD.AX',]
Start_Date = "2009-01-01"
End_Date = "2023-05-01"


multiData = GetAdjCloseQuotes(Tickers, Start_Date, End_Date)
multiData.head()


# Calculating daily returns
return_multiData = multiData.pct_change().dropna()
return_multiData


# Daily Volatility
TRADING_DAYS = 252
ann_vol_portfolio = return_multiData.rolling(window=TRADING_DAYS).std()*np.sqrt(TRADING_DAYS)
ann_vol_portfolio = ann_vol_portfolio.dropna()
ann_vol_portfolio.head()


# Create a figure and axis
fig, ax1 = plt.subplots()
# Plot returns on the first y-axis (left)
ax1.plot(return_multiData['^FTSE'], color='b', label='Returns')
ax1.set_xlabel('Time')
ax1.set_ylabel('Returns', color='b')
ax1.tick_params('y', colors='b')

# Create a second y-axis (right) for plotting volatility
ax2 = ax1.twinx()
ax2.plot(ann_vol_portfolio['^FTSE'], color='r', label='Volatility')
ax2.set_ylabel('Volatility', color='r')
ax2.tick_params('y', colors='r')

# Add legends
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
lines = lines1 + lines2
labels = labels1 + labels2
ax1.legend(lines, labels, loc='upper left')

<matplotlib.legend.Legend at 0x7cbb56a92a10>


# Sharpe_ratio
sharpe_ratio = return_multiData.mean()/ann_vol_portfolio
sharpe_ratio.tail()


# Cumulative Returns
cumprod_daily_return = (1 + return_multiData).cumprod()
cumprod_daily_return.head()


# heatmap of equity
sns.heatmap(multiData.corr(), cmap = 'coolwarm', annot = True)
plt.title('Correlations Heatmap')
plt.show()


fig, axes = plt.subplots(1, 3, sharex=True, figsize=(20,5))

sns.lineplot(data=ann_vol_portfolio, dashes=False, ax=axes[0])
axes[0].set_ylabel('Volatility')
axes[0].set_title('Portfolio Annualized Volatilty')


sns.lineplot(data=cumprod_daily_return, dashes=False, ax=axes[1])
axes[1].set_ylabel('Cumulative return')
axes[1].set_title('Indices Cumulative Returns')

sns.lineplot(data=sharpe_ratio, dashes=False, ax=axes[2])
axes[2].set_ylabel('sharpe_ratio')
axes[2].set_title('Indices sharpe_ratio')

Text(0.5, 1.0, 'Indices sharpe_ratio')


ann_vol_portfolio= (ann_vol_portfolio * 100).round()
ann_vol_portfolio.head()


vol_bins = [0, 10, 20, 30, 40, 50, 60]
vol_labels = ['0-9','10-19','20-29','30-39','40-49','50-59']
ann_vol_portfolio_2=pd.DataFrame()
ann_vol_portfolio_2['ftse_group'] = pd.cut(ann_vol_portfolio['^FTSE'], bins=vol_bins, labels=vol_labels, right=False)
ann_vol_portfolio_2['gspc_group'] = pd.cut(ann_vol_portfolio['^GSPC'], bins=vol_bins, labels=vol_labels, right=False)
ann_vol_portfolio_2['hsi_group'] = pd.cut(ann_vol_portfolio['^HSI'], bins=vol_bins, labels=vol_labels, right=False)
ann_vol_portfolio_2['nsei_group'] = pd.cut(ann_vol_portfolio['^NSEI'], bins=vol_bins, labels=vol_labels, right=False)
ann_vol_portfolio_2['gld_group'] = pd.cut(ann_vol_portfolio['GLD'], bins=vol_bins, labels=vol_labels, right=False)
ann_vol_portfolio_2['gold.ax_group'] = pd.cut(ann_vol_portfolio['GOLD.AX'], bins=vol_bins, labels=vol_labels, right=False)
ann_vol_portfolio_2.tail()


# Histogram of equity
ann_vol_portfolio.plot.hist(bins=10);


# Count
count_df_1 = ((ann_vol_portfolio < 15) & (ann_vol_portfolio.diff() <10)).sum()
count_df_2 = (((20 > ann_vol_portfolio) & (ann_vol_portfolio >= 15)) | ((20 > ann_vol_portfolio.diff()) & (ann_vol_portfolio.diff() > 15))).sum()
count_df_3 = (((25 > ann_vol_portfolio) & (ann_vol_portfolio >= 20)) | ((30 > ann_vol_portfolio.diff()) & (ann_vol_portfolio.diff() > 20))).sum()
count_df_4 = (((27 > ann_vol_portfolio) & (ann_vol_portfolio >= 25)) | ((15 > ann_vol_portfolio.diff()) & (ann_vol_portfolio.diff() > 10))).sum()
count_df_5 = ((ann_vol_portfolio  >=  27) | (ann_vol_portfolio.diff() > 25)).sum()


plt.figure(figsize=(15, 20))
plt.subplot(3,2,1)
count_df_1.plot.bar()
plt.ylabel('Count')
plt.title('CASE 1')

plt.subplot(3,2,2)
count_df_2.plot.bar()
plt.ylabel('Count')
plt.title('CASE 2')

plt.subplot(3,2,3)
count_df_3.plot.bar()
plt.ylabel('Count')
plt.title('CASE 3')

plt.subplot(3,2,4)
count_df_4.plot.bar()
plt.ylabel('Count')
plt.title('CASE 4')

plt.subplot(3,2,5)
count_df_5.plot.bar()
plt.ylabel('Count')
plt.title('CASE 5')

plt.show()


ann_vol_portfolio=ann_vol_portfolio.reset_index()
return_multiData=return_multiData.reset_index()


filtered_dates1 = ann_vol_portfolio[((ann_vol_portfolio['^FTSE'] < 15) & (ann_vol_portfolio['^FTSE'].diff() <10))]['DateTime']
filtered_dates4  = ann_vol_portfolio[(((27 > ann_vol_portfolio['^FTSE']) & (ann_vol_portfolio['^FTSE'] >= 25)) | ((15 > ann_vol_portfolio['^FTSE'].diff()) & (ann_vol_portfolio['^FTSE'].diff() > 10)))]['DateTime']
filtered_dates3  = ann_vol_portfolio[(((25 > ann_vol_portfolio['^FTSE']) & (ann_vol_portfolio['^FTSE'] >= 20)) | ((30 > ann_vol_portfolio['^FTSE'].diff()) & (ann_vol_portfolio['^FTSE'].diff() > 20)))]['DateTime']
filtered_dates2  = ann_vol_portfolio[(((20 > ann_vol_portfolio['^FTSE']) & (ann_vol_portfolio['^FTSE'] >= 15)) | ((20 > ann_vol_portfolio['^FTSE'].diff()) & (ann_vol_portfolio['^FTSE'].diff() > 15)))]['DateTime']
filtered_dates5  = ann_vol_portfolio[((ann_vol_portfolio['^FTSE']  >=  27) | (ann_vol_portfolio['^FTSE'].diff() >25))]['DateTime']


# Dates fullfilling the condition
def selected_returns_dataframe(filtered_dates):
  selected_returns = []
  for date in filtered_dates:
    return_value = return_multiData.loc[return_multiData['DateTime'] == date, '^FTSE'].values
    if len(return_value) > 0:
        selected_returns.append((date, return_value[0]))

  selected_return=pd.DataFrame(selected_returns)
  selected_return.set_axis(['Date', 'Portfolio_Return_ftse'], axis=1, inplace=True)
  return selected_return


def filtered_return_dataframe(selected_return_df):
  filtered_return_df = return_multiData[~return_multiData['DateTime'].isin(selected_return_df['Date'])]
  filtered_return_df1=pd.DataFrame()
  filtered_return_df1=filtered_return_df[['DateTime','^FTSE']]
  return filtered_return_df1


case1_ftse=selected_returns_dataframe(filtered_dates1)
case1_ftse.head()


# Comparing filtered dates with the main return dataframes dates and extract date and return from return dataframe which is not fulfilling condition
filt_case1_ftse= filtered_return_dataframe(case1_ftse)
filt_case1_ftse.head()


# cumulative product of return which is not satisfied the condition 1
filt_case1_ftse=filt_case1_ftse.set_index('DateTime')
filt_case1_ftse_cumpro=(1+filt_case1_ftse).cumprod()
filt_case1_ftse_cumpro.head()


case1_ftse.plot.hist()

<Axes: ylabel='Frequency'>


selected_return1_ftse=case1_ftse.set_index('Date')


def price_parameters(df_ser):
    return round(df_ser.mean(),2),round(df_ser.std(),2),round(df_ser.skew(),2),round(df_ser.kurtosis(),2)

#Plot distribution of series
def plot_dist(df):
    figure, axis = plt.subplots(1, 2)
    figure.set_figheight(10)
    figure.set_figwidth(20)
    mean,std,skew,kurt=price_parameters(df['Portfolio_Return_ftse']*100)
    sns.lineplot(data=df['Portfolio_Return_ftse']*100,ax=axis[0])
    axis[0].axhline(mean,color='r',ls='--')
    axis[0].set_title('Daily return chart(%), Total Returns:'+str(round(np.product(1+df['Portfolio_Return_ftse'])-1,2)*100)+'% Std-dev:'+str(std)+'%')
    sns.distplot(df['Portfolio_Return_ftse']*100,bins=50,ax=axis[1])
    x_axis=np.arange(min(df['Portfolio_Return_ftse']*100),max(df['Portfolio_Return_ftse']*100),0.0001)
    axis[1].axvline(mean,color='r',ls='--')
    axis[1].axvline(norm.ppf(0.05, loc=mean, scale=std),ls='--',color='r')
    axis[1].axvline(norm.ppf(0.95, loc=mean, scale=std),ls='--',color='r')
    axis[1].plot(x_axis,norm.pdf(x_axis, loc=mean, scale=std),ls='--',color='r')
    axis[1].set_title('Returns distribution, Mean:'+str(mean)+' std-dev:'+str(std)+' Skew:'+str(skew)+' Kurtosis:'+str(kurt))


plot_dist(selected_return1_ftse)


case2_ftse= selected_returns_dataframe(filtered_dates2)
case2_ftse.head()


filt_case2_ftse= filtered_return_dataframe(case2_ftse)
filt_case2_ftse.head()


# cumulative product of return which is not satisfied the condition 2
filt_case2_ftse=filt_case2_ftse.set_index('DateTime')
filt_case2_ftse_cumpro=(1+filt_case2_ftse).cumprod()
filt_case2_ftse_cumpro.head()


# Histogram of ftse
case2_ftse.plot.hist()

<Axes: ylabel='Frequency'>


selected_return2_ftse=case2_ftse.set_index('Date')
plot_dist(selected_return2_ftse)


case3_ftse= selected_returns_dataframe(filtered_dates3)
case3_ftse.head()


filt_case3_ftse= filtered_return_dataframe(case3_ftse)
filt_case3_ftse.head()


# cumulative product of return which is not satisfied the condition 3
filt_case3_ftse=filt_case3_ftse.set_index('DateTime')
filt_case3_ftse_cumpro=(1+filt_case3_ftse).cumprod()
filt_case3_ftse_cumpro.head()


# Histogram of ftse
case3_ftse.plot.hist()

<Axes: ylabel='Frequency'>


selected_return3_ftse=case3_ftse.set_index('Date')
plot_dist(selected_return3_ftse)


case4_ftse= selected_returns_dataframe(filtered_dates4)
case4_ftse.head()


filt_case4_ftse= filtered_return_dataframe(case4_ftse)
filt_case4_ftse.head()


# cumulative product of return which is not satisfied the condition 4
filt_case4_ftse=filt_case4_ftse.set_index('DateTime')
filt_case4_ftse_cumpro=(1+filt_case4_ftse).cumprod()
filt_case4_ftse_cumpro.head()


# Histogram of ftse
case4_ftse.plot.hist()

<Axes: ylabel='Frequency'>


selected_return4_ftse=case4_ftse.set_index('Date')
plot_dist(selected_return4_ftse)


case5_ftse= selected_returns_dataframe(filtered_dates5)
case5_ftse.head()


filt_case5_ftse= filtered_return_dataframe(case5_ftse)
filt_case5_ftse.head()


# cumulative product of return which is not satisfied the condition 5
filt_case5_ftse=filt_case5_ftse.set_index('DateTime')
filt_case5_ftse_cumpro=(1+filt_case5_ftse).cumprod()
filt_case5_ftse_cumpro.head()


# Histogram of ftse
case5_ftse.plot.hist()

<Axes: ylabel='Frequency'>


selected_return5_ftse=case5_ftse.set_index('Date')
plot_dist(selected_return5_ftse)


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^FTSE'], color='blue', label='^FTSE')


# Create a mask for the condition
mask1 = (ann_vol_portfolio['^FTSE'] < 15) & (ann_vol_portfolio['^FTSE'].diff() <10)

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^FTSE'], where=mask1, color='lightgray',label='case 1')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('FTSE')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^FTSE'], color='yellow', label='FTSE')


# Create a mask for the condition
mask2 = (((27 > ann_vol_portfolio['^FTSE']) & (ann_vol_portfolio['^FTSE'] >= 25)) | ((15 > ann_vol_portfolio['^FTSE'].diff()) & (ann_vol_portfolio['^FTSE'].diff() > 10)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^FTSE'], where=mask2, color='lightgray',label='case 2')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('FTSE')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^FTSE'], color='green', label='FTSE')


# Create a mask for the condition
mask3 = (((25 > ann_vol_portfolio['^FTSE']) & (ann_vol_portfolio['^FTSE'] >= 20)) | ((30 > ann_vol_portfolio['^FTSE'].diff()) & (ann_vol_portfolio['^FTSE'].diff() > 20)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^FTSE'], where=mask3, color='lightgray',label='case 3')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('FTSE')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^FTSE'], color='red', label='FTSE')


# Create a mask for the condition
mask4 = (((20 > ann_vol_portfolio['^FTSE']) & (ann_vol_portfolio['^FTSE'] >= 15)) | ((20 > ann_vol_portfolio['^FTSE'].diff()) & (ann_vol_portfolio['^FTSE'].diff() > 15)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^FTSE'], where=mask4, color='lightgray',label='case 4')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('FTSE')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^FTSE'], color='orange', label='FTSE')


# Create a mask for the condition
mask5 = (ann_vol_portfolio['^FTSE'] >= 27) | (ann_vol_portfolio['^FTSE'].diff() > 25)

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^FTSE'], where=mask5, color='lightgray',label='case 5')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('FTSE')
ax.legend(loc='upper right')

# Show the plot
plt.show()


#Plot distribution of series
def plot_dist(df):
    figure, axis = plt.subplots(1, 2)
    figure.set_figheight(10)
    figure.set_figwidth(20)
    mean,std,skew,kurt=price_parameters(df['Portfolio_Return_gspc']*100)
    sns.lineplot(data=df['Portfolio_Return_gspc']*100,ax=axis[0])
    axis[0].axhline(mean,color='r',ls='--')
    axis[0].set_title('Daily return chart(%), Total Returns:'+str(round(np.product(1+df['Portfolio_Return_gspc'])-1,2)*100)+'% Std-dev:'+str(std)+'%')
    sns.distplot(df['Portfolio_Return_gspc']*100,bins=50,ax=axis[1])
    x_axis=np.arange(min(df['Portfolio_Return_gspc']*100),max(df['Portfolio_Return_gspc']*100),0.0001)
    axis[1].axvline(mean,color='r',ls='--')
    axis[1].axvline(norm.ppf(0.05, loc=mean, scale=std),ls='--',color='r')
    axis[1].axvline(norm.ppf(0.95, loc=mean, scale=std),ls='--',color='r')
    axis[1].plot(x_axis,norm.pdf(x_axis, loc=mean, scale=std),ls='--',color='r')
    axis[1].set_title('Returns distribution, Mean:'+str(mean)+' std-dev:'+str(std)+' Skew:'+str(skew)+' Kurtosis:'+str(kurt))


filtered_dates1 = ann_vol_portfolio[((ann_vol_portfolio['^GSPC'] < 15) & (ann_vol_portfolio['^GSPC'].diff() <10))]['DateTime']
filtered_dates4  = ann_vol_portfolio[(((27 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 25)) | ((15 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 10)))]['DateTime']
filtered_dates3  = ann_vol_portfolio[(((25 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 20)) | ((30 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 20)))]['DateTime']
filtered_dates2  = ann_vol_portfolio[(((20 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 15)) | ((20 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 15)))]['DateTime']
filtered_dates5  = ann_vol_portfolio[((ann_vol_portfolio['^GSPC']  >=  27) | (ann_vol_portfolio['^GSPC'].diff() >25))]['DateTime']


# Dates fullfilling the condition
def selected_returns_dataframe(filtered_dates):
  selected_returns = []
  for date in filtered_dates:
    return_value = return_multiData.loc[return_multiData['DateTime'] == date, '^GSPC'].values
    if len(return_value) > 0:
        selected_returns.append((date, return_value[0]))

  selected_return=pd.DataFrame(selected_returns)
  selected_return.set_axis(['Date', 'Portfolio_Return_gspc'], axis=1, inplace=True)
  return selected_return


def filtered_return_dataframe(selected_return_df):
  filtered_return_df = return_multiData[~return_multiData['DateTime'].isin(selected_return_df['Date'])]
  filtered_return_df1=pd.DataFrame()
  filtered_return_df1=filtered_return_df[['DateTime','^GSPC']]
  return filtered_return_df1


case1_gspc=selected_returns_dataframe(filtered_dates1)
case1_gspc.head()


# Comparing filtered dates with the main return dataframes dates and extract date and return from return dataframe which is not fulfilling condition

filt_case1_gspc= filtered_return_dataframe(case1_gspc)
filt_case1_gspc.head()


# cumulative product of return which is not satisfied the condition 1
filt_case1_gspc=filt_case1_gspc.set_index('DateTime')
filt_case1_gspc_cumpro=(1+filt_case1_gspc).cumprod()
filt_case1_gspc_cumpro.head()


case1_gspc.plot.hist()

<Axes: ylabel='Frequency'>


selected_return1_gspc=case1_gspc.set_index('Date')
plot_dist(selected_return1_gspc)


case2_gspc= selected_returns_dataframe(filtered_dates2)
case2_gspc.head()


filt_case2_gspc= filtered_return_dataframe(case2_gspc)
filt_case2_gspc.head()


# cumulative product of return which is not satisfied the condition 2
filt_case2_gspc=filt_case2_gspc.set_index('DateTime')
filt_case2_gspc_cumpro=(1+filt_case2_gspc).cumprod()
filt_case2_gspc_cumpro.head()


# Histogram of gspc
case2_gspc.plot.hist()

<Axes: ylabel='Frequency'>


selected_return2_gspc=case2_gspc.set_index('Date')
plot_dist(selected_return2_gspc)


case3_gspc= selected_returns_dataframe(filtered_dates3)
case3_gspc.head()


filt_case3_gspc= filtered_return_dataframe(case3_gspc)
filt_case3_gspc.head()


# cumulative product of return which is not satisfied the condition 3
filt_case3_gspc=filt_case3_gspc.set_index('DateTime')
filt_case3_gspc_cumpro=(1+filt_case3_gspc).cumprod()
filt_case3_gspc_cumpro.head()


# Histogram of gspc
case3_gspc.plot.hist()

<Axes: ylabel='Frequency'>


selected_return3_gspc=case3_gspc.set_index('Date')
plot_dist(selected_return3_gspc)


case4_gspc= selected_returns_dataframe(filtered_dates4)
case4_gspc.head()


filt_case4_gspc= filtered_return_dataframe(case4_gspc)
filt_case4_gspc.head()


# cumulative product of return which is not satisfied the condition 4
filt_case4_gspc=filt_case4_gspc.set_index('DateTime')
filt_case4_gspc_cumpro=(1+filt_case4_gspc).cumprod()
filt_case4_gspc_cumpro.head()


# Histogram of gspc
case4_gspc.plot.hist()

<Axes: ylabel='Frequency'>


selected_return4_gspc=case4_gspc.set_index('Date')
plot_dist(selected_return4_gspc)


case5_gspc= selected_returns_dataframe(filtered_dates5)
case5_gspc.head()


filt_case5_gspc= filtered_return_dataframe(case5_gspc)
filt_case5_gspc.head()


# cumulative product of return which is not satisfied the condition 5
filt_case5_gspc=filt_case5_gspc.set_index('DateTime')
filt_case5_gspc_cumpro=(1+filt_case5_gspc).cumprod()
filt_case5_gspc_cumpro.head()


# Histogram of gspc
case5_gspc.plot.hist()

<Axes: ylabel='Frequency'>


selected_return5_gspc=case5_gspc.set_index('Date')
plot_dist(selected_return5_gspc)


# Create a figure and axis
fig, ax = plt.subplots()

# Plot the entire '^GSPC' column
ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^GSPC'], color='blue', label='GSPC')


# Create a mask for the condition
mask1 = (ann_vol_portfolio['^GSPC'] < 15) & (ann_vol_portfolio['^GSPC'].diff() <10)

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^GSPC'], where=mask1, color='lightgray',label='case 1')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('GSPC')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

# Plot the entire '^GSPC' column
ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^GSPC'], color='yellow', label='GSPC')


# Create a mask for the condition
mask2 = (((27 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 25)) | ((15 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 10)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^GSPC'], where=mask2, color='lightgray',label='case 2')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('GSPC')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

# Plot the entire '^GSPC' column
ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^GSPC'], color='green', label='GSPC')


# Create a mask for the condition
mask3 = (((25 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 20)) | ((30 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 20)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^GSPC'], where=mask3, color='lightgray',label='case 3')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('GSPC')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

# Plot the entire '^GSPC' column
ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^GSPC'], color='red', label='GSPC')


# Create a mask for the condition
mask4 = (((20 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 15)) | ((20 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 15)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^GSPC'], where=mask4, color='lightgray',label='case 4')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('GSPC')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

# Plot the entire '^GSPC' column
ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^GSPC'], color='orange', label='GSPC')


# Create a mask for the condition
mask5 = (ann_vol_portfolio['^GSPC'] >= 27) | (ann_vol_portfolio['^GSPC'].diff() > 25)

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^GSPC'], where=mask5, color='lightgray',label='case 5')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('GSPC')
ax.legend(loc='upper right')

# Show the plot
plt.show()


#Plot distribution of series
def plot_dist(df):
    figure, axis = plt.subplots(1, 2)
    figure.set_figheight(10)
    figure.set_figwidth(20)
    mean,std,skew,kurt=price_parameters(df['Portfolio_Return_hsi']*100)
    sns.lineplot(data=df['Portfolio_Return_hsi']*100,ax=axis[0])
    axis[0].axhline(mean,color='r',ls='--')
    axis[0].set_title('Daily return chart(%), Total Returns:'+str(round(np.product(1+df['Portfolio_Return_hsi'])-1,2)*100)+'% Std-dev:'+str(std)+'%')
    sns.distplot(df['Portfolio_Return_hsi']*100,bins=50,ax=axis[1])
    x_axis=np.arange(min(df['Portfolio_Return_hsi']*100),max(df['Portfolio_Return_hsi']*100),0.0001)
    axis[1].axvline(mean,color='r',ls='--')
    axis[1].axvline(norm.ppf(0.05, loc=mean, scale=std),ls='--',color='r')
    axis[1].axvline(norm.ppf(0.95, loc=mean, scale=std),ls='--',color='r')
    axis[1].plot(x_axis,norm.pdf(x_axis, loc=mean, scale=std),ls='--',color='r')
    axis[1].set_title('Returns distribution, Mean:'+str(mean)+' std-dev:'+str(std)+' Skew:'+str(skew)+' Kurtosis:'+str(kurt))


filtered_dates1 = ann_vol_portfolio[((ann_vol_portfolio['^HSI'] < 15) & (ann_vol_portfolio['^HSI'].diff() <10))]['DateTime']
filtered_dates4  = ann_vol_portfolio[(((27 > ann_vol_portfolio['^HSI']) & (ann_vol_portfolio['^HSI'] >= 25)) | ((15 > ann_vol_portfolio['^HSI'].diff()) & (ann_vol_portfolio['^HSI'].diff() > 10)))]['DateTime']
filtered_dates3  = ann_vol_portfolio[(((25 > ann_vol_portfolio['^HSI']) & (ann_vol_portfolio['^HSI'] >= 20)) | ((30 > ann_vol_portfolio['^HSI'].diff()) & (ann_vol_portfolio['^HSI'].diff() > 20)))]['DateTime']
filtered_dates2  = ann_vol_portfolio[(((20 > ann_vol_portfolio['^HSI']) & (ann_vol_portfolio['^HSI'] >= 15)) | ((20 > ann_vol_portfolio['^HSI'].diff()) & (ann_vol_portfolio['^HSI'].diff() > 15)))]['DateTime']
filtered_dates5  = ann_vol_portfolio[((ann_vol_portfolio['^HSI']  >=  27) | (ann_vol_portfolio['^HSI'].diff() >25))]['DateTime']


# Dates fullfilling the condition
def selected_returns_dataframe(filtered_dates):
  selected_returns = []
  for date in filtered_dates:
    return_value = return_multiData.loc[return_multiData['DateTime'] == date, '^HSI'].values
    if len(return_value) > 0:
        selected_returns.append((date, return_value[0]))

  selected_return=pd.DataFrame(selected_returns)
  selected_return.set_axis(['Date', 'Portfolio_Return_hsi'], axis=1, inplace=True)
  return selected_return


def filtered_return_dataframe(selected_return_df):
  filtered_return_df = return_multiData[~return_multiData['DateTime'].isin(selected_return_df['Date'])]
  filtered_return_df1=pd.DataFrame()
  filtered_return_df1=filtered_return_df[['DateTime','^HSI']]
  return filtered_return_df1


case1_hsi=selected_returns_dataframe(filtered_dates1)
case1_hsi.head()


# Comparing filtered dates with the main return dataframes dates and extract date and return from return dataframe which is not fulfilling condition

filt_case1_hsi= filtered_return_dataframe(case1_hsi)
filt_case1_hsi.head()


# cumulative product of return which is not satisfied the condition 1
filt_case1_hsi=filt_case1_hsi.set_index('DateTime')
filt_case1_hsi_cumpro=(1+filt_case1_hsi).cumprod()
filt_case1_hsi_cumpro.head()


case1_hsi.plot.hist()

<Axes: ylabel='Frequency'>


selected_return1_hsi=case1_hsi.set_index('Date')
plot_dist(selected_return1_hsi)


case2_hsi= selected_returns_dataframe(filtered_dates2)
case2_hsi.head()


filt_case2_hsi= filtered_return_dataframe(case2_hsi)
filt_case2_hsi.head()


# cumulative product of return which is not satisfied the condition 2
filt_case2_hsi=filt_case2_hsi.set_index('DateTime')
filt_case2_hsi_cumpro=(1+filt_case2_hsi).cumprod()
filt_case2_hsi_cumpro.head()


# Histogram of hsi
case2_hsi.plot.hist()

<Axes: ylabel='Frequency'>


selected_return2_hsi=case2_hsi.set_index('Date')
plot_dist(selected_return2_hsi)


case3_hsi= selected_returns_dataframe(filtered_dates3)
case3_hsi.head()


filt_case3_hsi= filtered_return_dataframe(case3_hsi)
filt_case3_hsi.head()


# cumulative product of return which is not satisfied the condition 3
filt_case3_hsi=filt_case3_hsi.set_index('DateTime')
filt_case3_hsi_cumpro=(1+filt_case3_hsi).cumprod()
filt_case3_hsi_cumpro.head()


# Histogram of hsi
case3_hsi.plot.hist()

<Axes: ylabel='Frequency'>


selected_return3_hsi=case3_hsi.set_index('Date')
plot_dist(selected_return3_hsi)


case4_hsi= selected_returns_dataframe(filtered_dates4)
case4_hsi.head()


filt_case4_hsi= filtered_return_dataframe(case4_hsi)
filt_case4_hsi.head()


# cumulative product of return which is not satisfied the condition 4
filt_case4_hsi=filt_case4_hsi.set_index('DateTime')
filt_case4_hsi_cumpro=(1+filt_case4_hsi).cumprod()
filt_case4_hsi_cumpro.head()


# Histogram of hsi
case4_hsi.plot.hist()

<Axes: ylabel='Frequency'>


selected_return4_hsi=case4_hsi.set_index('Date')
plot_dist(selected_return4_hsi)


case5_hsi= selected_returns_dataframe(filtered_dates5)
case5_hsi.head()


filt_case5_hsi= filtered_return_dataframe(case5_hsi)
filt_case5_hsi.head()


# cumulative product of return which is not satisfied the condition 5
filt_case5_hsi=filt_case5_hsi.set_index('DateTime')
filt_case5_hsi_cumpro=(1+filt_case5_hsi).cumprod()
filt_case5_hsi_cumpro.head()


# Histogram of hsi
case5_hsi.plot.hist()

<Axes: ylabel='Frequency'>


selected_return5_hsi=case5_hsi.set_index('Date')
plot_dist(selected_return5_hsi)


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^HSI'], color='blue', label='HSI')


# Create a mask for the condition
mask1 = (ann_vol_portfolio['^HSI'] < 15) & (ann_vol_portfolio['^HSI'].diff() <10)

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^HSI'], where=mask1, color='lightgray',label='case 1')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('HSI')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^HSI'], color='yellow', label='HSI')


# Create a mask for the condition
mask2 = (((27 > ann_vol_portfolio['^HSI']) & (ann_vol_portfolio['^HSI'] >= 25)) | ((15 > ann_vol_portfolio['^HSI'].diff()) & (ann_vol_portfolio['^HSI'].diff() > 10)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^HSI'], where=mask2, color='lightgray',label='case 2')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('HSI')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^HSI'], color='green', label='HSI')


# Create a mask for the condition
mask3 = (((25 > ann_vol_portfolio['^HSI']) & (ann_vol_portfolio['^HSI'] >= 20)) | ((30 > ann_vol_portfolio['^HSI'].diff()) & (ann_vol_portfolio['^HSI'].diff() > 20)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^HSI'], where=mask3, color='lightgray',label='case 3')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('HSI')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^HSI'], color='red', label='HSI')


# Create a mask for the condition
mask4 = (((20 > ann_vol_portfolio['^HSI']) & (ann_vol_portfolio['^HSI'] >= 15)) | ((20 > ann_vol_portfolio['^HSI'].diff()) & (ann_vol_portfolio['^HSI'].diff() > 15)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^HSI'], where=mask4, color='lightgray',label='case 4')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('HSI')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^HSI'], color='orange', label='HSI')


# Create a mask for the condition
mask5 = (ann_vol_portfolio['^HSI'] >= 27) | (ann_vol_portfolio['^HSI'].diff() > 25)

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^HSI'], where=mask5, color='lightgray',label='case 5')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('HSI')
ax.legend(loc='upper right')

# Show the plot
plt.show()


#Plot distribution of series
def plot_dist(df):
    figure, axis = plt.subplots(1, 2)
    figure.set_figheight(10)
    figure.set_figwidth(20)
    mean,std,skew,kurt=price_parameters(df['Portfolio_Return_nsei']*100)
    sns.lineplot(data=df['Portfolio_Return_nsei']*100,ax=axis[0])
    axis[0].axhline(mean,color='r',ls='--')
    axis[0].set_title('Daily return chart(%), Total Returns:'+str(round(np.product(1+df['Portfolio_Return_nsei'])-1,2)*100)+'% Std-dev:'+str(std)+'%')
    sns.distplot(df['Portfolio_Return_nsei']*100,bins=50,ax=axis[1])
    x_axis=np.arange(min(df['Portfolio_Return_nsei']*100),max(df['Portfolio_Return_nsei']*100),0.0001)
    axis[1].axvline(mean,color='r',ls='--')
    axis[1].axvline(norm.ppf(0.05, loc=mean, scale=std),ls='--',color='r')
    axis[1].axvline(norm.ppf(0.95, loc=mean, scale=std),ls='--',color='r')
    axis[1].plot(x_axis,norm.pdf(x_axis, loc=mean, scale=std),ls='--',color='r')
    axis[1].set_title('Returns distribution, Mean:'+str(mean)+' std-dev:'+str(std)+' Skew:'+str(skew)+' Kurtosis:'+str(kurt))


filtered_dates1 = ann_vol_portfolio[((ann_vol_portfolio['^NSEI'] < 15) & (ann_vol_portfolio['^NSEI'].diff() <10))]['DateTime']
filtered_dates4  = ann_vol_portfolio[(((27 > ann_vol_portfolio['^NSEI']) & (ann_vol_portfolio['^NSEI'] >= 25)) | ((15 > ann_vol_portfolio['^NSEI'].diff()) & (ann_vol_portfolio['^NSEI'].diff() > 10)))]['DateTime']
filtered_dates3  = ann_vol_portfolio[(((25 > ann_vol_portfolio['^NSEI']) & (ann_vol_portfolio['^NSEI'] >= 20)) | ((30 > ann_vol_portfolio['^NSEI'].diff()) & (ann_vol_portfolio['^NSEI'].diff() > 20)))]['DateTime']
filtered_dates2  = ann_vol_portfolio[(((20 > ann_vol_portfolio['^NSEI']) & (ann_vol_portfolio['^NSEI'] >= 15)) | ((20 > ann_vol_portfolio['^NSEI'].diff()) & (ann_vol_portfolio['^NSEI'].diff() > 15)))]['DateTime']
filtered_dates5  = ann_vol_portfolio[((ann_vol_portfolio['^NSEI']  >=  27) | (ann_vol_portfolio['^NSEI'].diff() >25))]['DateTime']


# Dates fullfilling the condition
def selected_returns_dataframe(filtered_dates):
  selected_returns = []
  for date in filtered_dates:
    return_value = return_multiData.loc[return_multiData['DateTime'] == date, '^NSEI'].values
    if len(return_value) > 0:
        selected_returns.append((date, return_value[0]))

  selected_return=pd.DataFrame(selected_returns)
  selected_return.set_axis(['Date', 'Portfolio_Return_nsei'], axis=1, inplace=True)
  return selected_return


def filtered_return_dataframe(selected_return_df):
  filtered_return_df = return_multiData[~return_multiData['DateTime'].isin(selected_return_df['Date'])]
  filtered_return_df1=pd.DataFrame()
  filtered_return_df1=filtered_return_df[['DateTime','^NSEI']]
  return filtered_return_df1


case1_nsei=selected_returns_dataframe(filtered_dates1)
case1_nsei.head()


# Comparing filtered dates with the main return dataframes dates and extract date and return from return dataframe which is not fulfilling condition

filt_case1_nsei= filtered_return_dataframe(case1_nsei)
filt_case1_nsei.head()


# cumulative product of return which is not satisfied the condition 1
filt_case1_nsei=filt_case1_nsei.set_index('DateTime')
filt_case1_nsei_cumpro=(1+filt_case1_nsei).cumprod()
filt_case1_nsei_cumpro.head()


case1_nsei.plot.hist()

<Axes: ylabel='Frequency'>


selected_return1_nsei=case1_nsei.set_index('Date')
plot_dist(selected_return1_nsei)


case2_nsei= selected_returns_dataframe(filtered_dates2)
case2_nsei.head()


filt_case2_nsei= filtered_return_dataframe(case2_nsei)
filt_case2_nsei.head()


# cumulative product of return which is not satisfied the condition 2
filt_case2_nsei=filt_case2_nsei.set_index('DateTime')
filt_case2_nsei_cumpro=(1+filt_case2_nsei).cumprod()
filt_case2_nsei_cumpro.head()


# Histogram of nsei
case2_nsei.plot.hist()

<Axes: ylabel='Frequency'>


selected_return2_nsei=case2_nsei.set_index('Date')
plot_dist(selected_return2_nsei)


case3_nsei= selected_returns_dataframe(filtered_dates3)
case3_nsei.head()


filt_case3_nsei= filtered_return_dataframe(case3_nsei)
filt_case3_nsei.head()


# cumulative product of return which is not satisfied the condition 3
filt_case3_nsei=filt_case3_nsei.set_index('DateTime')
filt_case3_nsei_cumpro=(1+filt_case3_nsei).cumprod()
filt_case3_nsei_cumpro.head()


# Histogram of nsei
case3_nsei.plot.hist()

<Axes: ylabel='Frequency'>


selected_return3_nsei=case3_nsei.set_index('Date')
plot_dist(selected_return3_nsei)


case4_nsei= selected_returns_dataframe(filtered_dates4)
case4_nsei.head()


filt_case4_nsei= filtered_return_dataframe(case4_nsei)
filt_case4_nsei.head()


# cumulative product of return which is not satisfied the condition 4
filt_case4_nsei=filt_case4_nsei.set_index('DateTime')
filt_case4_nsei_cumpro=(1+filt_case4_nsei).cumprod()
filt_case4_nsei_cumpro.head()


# Histogram of nsei
case4_nsei.plot.hist()

<Axes: ylabel='Frequency'>


selected_return4_nsei=case4_nsei.set_index('Date')
plot_dist(selected_return4_nsei)


case5_nsei= selected_returns_dataframe(filtered_dates5)
case5_nsei.head()


filt_case5_nsei= filtered_return_dataframe(case5_nsei)
filt_case5_nsei.head()


# cumulative product of return which is not satisfied the condition 5
filt_case5_nsei=filt_case5_nsei.set_index('DateTime')
filt_case5_nsei_cumpro=(1+filt_case5_nsei).cumprod()
filt_case5_nsei_cumpro.head()


# Histogram of nsei
case5_nsei.plot.hist()

<Axes: ylabel='Frequency'>


selected_return5_nsei=case5_nsei.set_index('Date')
plot_dist(selected_return5_nsei)


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^NSEI'], color='blue', label='NSEI')


# Create a mask for the condition
mask1 = (ann_vol_portfolio['^NSEI'] < 15) & (ann_vol_portfolio['^NSEI'].diff() <10)

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^NSEI'], where=mask1, color='lightgray',label='case 1')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('NSEI')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^NSEI'], color='yellow', label='NSEI')


# Create a mask for the condition
mask2 = (((27 > ann_vol_portfolio['^NSEI']) & (ann_vol_portfolio['^NSEI'] >= 25)) | ((15 > ann_vol_portfolio['^NSEI'].diff()) & (ann_vol_portfolio['^NSEI'].diff() > 10)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^NSEI'], where=mask2, color='lightgray',label='case 2')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('NSEI')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^NSEI'], color='green', label='NSEI')


# Create a mask for the condition
mask3 = (((25 > ann_vol_portfolio['^NSEI']) & (ann_vol_portfolio['^NSEI'] >= 20)) | ((30 > ann_vol_portfolio['^NSEI'].diff()) & (ann_vol_portfolio['^NSEI'].diff() > 20)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^NSEI'], where=mask3, color='lightgray',label='case 3')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('NSEI')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^NSEI'], color='red', label='NSEI')


# Create a mask for the condition
mask4 = (((20 > ann_vol_portfolio['^NSEI']) & (ann_vol_portfolio['^NSEI'] >= 15)) | ((20 > ann_vol_portfolio['^NSEI'].diff()) & (ann_vol_portfolio['^NSEI'].diff() > 15)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^NSEI'], where=mask4, color='lightgray',label='case 4')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('NSEI')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['^NSEI'], color='orange', label='NSEI')


# Create a mask for the condition
mask5 = (ann_vol_portfolio['^NSEI'] >= 27) | (ann_vol_portfolio['^NSEI'].diff() > 25)

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['^NSEI'], where=mask5, color='lightgray',label='case 5')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('NSEI')
ax.legend(loc='upper right')

# Show the plot
plt.show()


#Plot distribution of series
def plot_dist(df):
    figure, axis = plt.subplots(1, 2)
    figure.set_figheight(10)
    figure.set_figwidth(20)
    mean,std,skew,kurt=price_parameters(df['Portfolio_Return_gld']*100)
    sns.lineplot(data=df['Portfolio_Return_gld']*100,ax=axis[0])
    axis[0].axhline(mean,color='r',ls='--')
    axis[0].set_title('Daily return chart(%), Total Returns:'+str(round(np.product(1+df['Portfolio_Return_gld'])-1,2)*100)+'% Std-dev:'+str(std)+'%')
    sns.distplot(df['Portfolio_Return_gld']*100,bins=50,ax=axis[1])
    x_axis=np.arange(min(df['Portfolio_Return_gld']*100),max(df['Portfolio_Return_gld']*100),0.0001)
    axis[1].axvline(mean,color='r',ls='--')
    axis[1].axvline(norm.ppf(0.05, loc=mean, scale=std),ls='--',color='r')
    axis[1].axvline(norm.ppf(0.95, loc=mean, scale=std),ls='--',color='r')
    axis[1].plot(x_axis,norm.pdf(x_axis, loc=mean, scale=std),ls='--',color='r')
    axis[1].set_title('Returns distribution, Mean:'+str(mean)+' std-dev:'+str(std)+' Skew:'+str(skew)+' Kurtosis:'+str(kurt))


filtered_dates1 = ann_vol_portfolio[((ann_vol_portfolio['^GSPC'] < 15) & (ann_vol_portfolio['^GSPC'].diff() <10))]['DateTime']
filtered_dates4  = ann_vol_portfolio[(((27 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 25)) | ((15 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 10)))]['DateTime']
filtered_dates3  = ann_vol_portfolio[(((25 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 20)) | ((30 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 20)))]['DateTime']
filtered_dates2  = ann_vol_portfolio[(((20 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 15)) | ((20 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 15)))]['DateTime']
filtered_dates5  = ann_vol_portfolio[((ann_vol_portfolio['^GSPC']  >=  27) | (ann_vol_portfolio['^GSPC'].diff() >25))]['DateTime']


# Dates fullfilling the condition
def selected_returns_dataframe(filtered_dates):
  selected_returns = []
  for date in filtered_dates:
    return_value = return_multiData.loc[return_multiData['DateTime'] == date, 'GLD'].values
    if len(return_value) > 0:
        selected_returns.append((date, return_value[0]))

  selected_return=pd.DataFrame(selected_returns)
  selected_return.set_axis(['Date', 'Portfolio_Return_gld'], axis=1, inplace=True)
  return selected_return


case1_gld=selected_returns_dataframe(filtered_dates1)
case1_gld.head()


case1_gld.plot.hist()

<Axes: ylabel='Frequency'>


selected_return1_gld=case1_gld.set_index('Date')
plot_dist(selected_return1_gld)


case2_gld= selected_returns_dataframe(filtered_dates2)
case2_gld.head()


# Histogram of gld
case2_gld.plot.hist()

<Axes: ylabel='Frequency'>


selected_return2_gld=case2_gld.set_index('Date')
plot_dist(selected_return2_gld)


case3_gld= selected_returns_dataframe(filtered_dates3)
case3_gld.head()


# Histogram of gld
case3_gld.plot.hist()

<Axes: ylabel='Frequency'>


selected_return3_gld=case3_gld.set_index('Date')
plot_dist(selected_return3_gld)


case4_gld= selected_returns_dataframe(filtered_dates4)
case4_gld.head()


# Histogram of gld
case4_gld.plot.hist()

<Axes: ylabel='Frequency'>


selected_return4_gld=case4_gld.set_index('Date')
plot_dist(selected_return4_gld)


case5_gld= selected_returns_dataframe(filtered_dates5)
case5_gld.head()


# Histogram of gld
case5_gld.plot.hist()

<Axes: ylabel='Frequency'>


selected_return5_gld=case5_gld.set_index('Date')
plot_dist(selected_return5_gld)


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['GLD'], color='blue', label='GLD')


# Create a mask for the condition
mask1 = (ann_vol_portfolio['^GSPC'] < 15) & (ann_vol_portfolio['^GSPC'].diff() <10)

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['GLD'], where=mask1, color='lightgray',label='case 1')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('GOLD')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['GLD'], color='yellow', label='GLD')


# Create a mask for the condition
mask2 = (((27 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 25)) | ((15 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 10)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['GLD'], where=mask2, color='lightgray',label='case 2')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('GOLD')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['GLD'], color='green', label='GLD')


# Create a mask for the condition
mask3 = (((25 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 20)) | ((30 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 20)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['GLD'], where=mask3, color='lightgray',label='case 3')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('GOLD')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['GLD'], color='red', label='GLD')


# Create a mask for the condition
mask4 = (((20 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 15)) | ((20 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 15)))

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['GLD'], where=mask4, color='lightgray',label='case 4')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('GOLD')
ax.legend(loc='upper right')

# Show the plot
plt.show()


# Create a figure and axis
fig, ax = plt.subplots()

ax.plot(ann_vol_portfolio['DateTime'],ann_vol_portfolio['GLD'], color='orange', label='GLD')


# Create a mask for the condition
mask5 = (ann_vol_portfolio['^GSPC'] >= 27) | (ann_vol_portfolio['^GSPC'].diff() > 25)

# Shade the regions where the condition is met
ax.fill_between(ann_vol_portfolio['DateTime'], ann_vol_portfolio['GLD'], where=mask5, color='lightgray',label='case 5')


# Set labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Volatility')
ax.set_title('GOLD')
ax.legend(loc='upper right')

# Show the plot
plt.show()


from google.colab import files
uploaded = files.upload()

Saving China 10-Year Bond Yield Historical Data.csv to China 10-Year Bond Yield Historical Data.csv
Saving India 10-Year Bond Yield Historical Data.csv to India 10-Year Bond Yield Historical Data.csv
Saving United Kingdom 10-Year Bond Yield Historical Data.csv to United Kingdom 10-Year Bond Yield Historical Data.csv
Saving United States 10-Year Bond Yield Historical Data.csv to United States 10-Year Bond Yield Historical Data.csv


df_data_us=pd.read_csv('United States 10-Year Bond Yield Historical Data.csv')
df_data_ind=pd.read_csv('India 10-Year Bond Yield Historical Data.csv')
df_data_chi=pd.read_csv('China 10-Year Bond Yield Historical Data.csv')
df_data_uk=pd.read_csv('United Kingdom 10-Year Bond Yield Historical Data.csv')


df_data_us=df_data_us.set_index('Date')
df_data_chi=df_data_chi.set_index('Date')
df_data_ind=df_data_ind.set_index('Date')
df_data_uk=df_data_uk.set_index('Date')


df_bond=pd.DataFrame()
df_bond['US_Price']=df_data_us['Price']
df_bond['IN_Price']=df_data_ind['Price']
df_bond['UK_Price']=df_data_uk['Price']

df_bond['CHI_Price']=df_data_chi['Price']

df_bond.head()


df_bond=df_bond.reset_index()
df_bond


df_bond['Date'] = pd.to_datetime(df_bond['Date'], infer_datetime_format=True)
df_bond=df_bond.set_index('Date')
df_bond


df_bond=df_bond.dropna()
df_bond.head()


df_bond_return=df_bond.pct_change()
df_bond_return.fillna(0, inplace=True)
df_bond_return.head()


ax1 = plt.subplot(4,2,1)
ax2 = plt.subplot(4,2,2)
ax3 = plt.subplot(4,2,3)
ax4 = plt.subplot(4,2,4)



ax1.plot(df_bond_return['US_Price'], linewidth = 2.5)
ax1.set_ylabel('Return')
ax1.set_title('US Bond Price')

ax2.plot(df_bond_return['IN_Price'], color = 'orange', linewidth = 2.5)
ax2.set_ylabel('Return')
ax2.set_title('India Bond Price')

ax3.plot(df_bond_return['UK_Price'], color = 'green', linewidth = 2.5)
ax3.set_ylabel('Return')
ax3.set_title('United Kingdom Bond Price')



ax4.plot(df_bond_return['CHI_Price'], color = '#DEB887', linewidth = 2.5)
ax4.set_ylabel('Return')
ax4.set_title('China Bond Price')


plt.show()


df_bond_return=df_bond_return.reset_index()
df_bond_return.head()


def drawdown(return_series: pd.Series):
    """Takes a time series of asset returns.
       returns a DataFrame with columns for
       the wealth index,
       the previous peaks, and
       the percentage drawdown
    """
    wealth_index = 1000*(1+return_series).cumprod()
    previous_peaks = wealth_index.cummax()
    drawdowns = (wealth_index - previous_peaks)/previous_peaks
    return pd.DataFrame({"Wealth": wealth_index,
                         "Previous Peak": previous_peaks,
                         "Drawdown": drawdowns})


filtered_dates1 = ann_vol_portfolio[((ann_vol_portfolio['^FTSE'] < 15) & (ann_vol_portfolio['^FTSE'].diff() <10))]['DateTime']
filtered_dates4  = ann_vol_portfolio[(((27 > ann_vol_portfolio['^FTSE']) & (ann_vol_portfolio['^FTSE'] >= 25)) | ((15 > ann_vol_portfolio['^FTSE'].diff()) & (ann_vol_portfolio['^FTSE'].diff() > 10)))]['DateTime']
filtered_dates3  = ann_vol_portfolio[(((25 > ann_vol_portfolio['^FTSE']) & (ann_vol_portfolio['^FTSE'] >= 20)) | ((30 > ann_vol_portfolio['^FTSE'].diff()) & (ann_vol_portfolio['^FTSE'].diff() > 20)))]['DateTime']
filtered_dates2  = ann_vol_portfolio[(((20 > ann_vol_portfolio['^FTSE']) & (ann_vol_portfolio['^FTSE'] >= 15)) | ((20 > ann_vol_portfolio['^FTSE'].diff()) & (ann_vol_portfolio['^FTSE'].diff() > 15)))]['DateTime']
filtered_dates5  = ann_vol_portfolio[((ann_vol_portfolio['^FTSE']  >=  27) | (ann_vol_portfolio['^FTSE'].diff() >25))]['DateTime']


# Dates fullfilling the condition
def selected_returns_dataframe(filtered_dates):
  selected_returns = []
  for date in filtered_dates:
    return_value = df_bond_return.loc[df_bond_return['Date'] == date, 'UK_Price'].values
    if len(return_value) > 0:
        selected_returns.append((date, return_value[0]))

  selected_return=pd.DataFrame(selected_returns)
  selected_return.set_axis(['Date', 'Portfolio_Return_uk_bond'], axis=1, inplace=True)
  return selected_return


# Define weights
wts_c1 = [0.9, 0.1]
wts_c2 = [0.8, 0.2]
wts_c3 = [0.7, 0.3]
wts_c4 = [0.6, 0.4]
wts_c5 = [0.1, 0.9]
wts_b = [1, 0]


case1_uk=selected_returns_dataframe(filtered_dates1)
case1_uk.head()


# Histogram of bond
case1_uk.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_uk=case1_uk.set_index('Date')
selected_returns_bond_uk.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case1_ftse=case1_ftse.set_index('Date')
case1_uk=case1_uk.set_index('Date')


case1_data_uk_ftse = case1_ftse.join(case1_uk, how='inner')
case1_data_uk_ftse.head()


# UK
weighted_return_c1_uk = (wts_c1 * case1_data_uk_ftse)


#Cumulative Portfolio UK
portfolio_return_c1_uk = weighted_return_c1_uk.sum(axis=1)
cumprod_portfolio_c1_uk = (1 + portfolio_return_c1_uk).cumprod()


#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c1_uk, label='CASE 1(90E+10B)')

plt.legend(loc='upper left', fontsize=15)
plt.title("UK Portfolio Performance")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case2_uk=selected_returns_dataframe(filtered_dates2)
case2_uk.head()


# Histogram of bond
case2_uk.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_uk=case2_uk.set_index('Date')
selected_returns_bond_uk.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case2_ftse=case2_ftse.set_index('Date')
case2_uk=case2_uk.set_index('Date')


case2_data_uk_ftse = case2_ftse.join(case2_uk, how='inner')
case2_data_uk_ftse.head()


# UK

weighted_return_c2_uk = (wts_c2 * case2_data_uk_ftse)


#Cumulative Portfolio UK
portfolio_return_c2_uk = weighted_return_c2_uk.sum(axis=1)
cumprod_portfolio_c2_uk = (1 + portfolio_return_c2_uk).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c2_uk, label='CASE 2(80E+20B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("UK Portfolio Performance C2")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case3_uk=selected_returns_dataframe(filtered_dates3)
case3_uk.head()


# Histogram of bond
case3_uk.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_uk=case3_uk.set_index('Date')
selected_returns_bond_uk.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case3_ftse=case3_ftse.set_index('Date')
case3_uk=case3_uk.set_index('Date')


case3_data_uk_ftse = case3_ftse.join(case3_uk, how='inner')
case3_data_uk_ftse.head()


# UK
weighted_return_c3_uk = (wts_c3 * case3_data_uk_ftse)

#Cumulative Portfolio UK
portfolio_return_c3_uk = weighted_return_c3_uk.sum(axis=1)
cumprod_portfolio_c3_uk = (1 + portfolio_return_c3_uk).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c3_uk, label='CASE 3(70E+30B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("UK Portfolio Performance C3")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case4_uk=selected_returns_dataframe(filtered_dates4)
case4_uk.head()


# Histogram of bond
case4_uk.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_uk=case4_uk.set_index('Date')
selected_returns_bond_uk.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case4_ftse=case4_ftse.set_index('Date')
case4_uk=case4_uk.set_index('Date')


case4_data_uk_ftse = case4_ftse.join(case4_uk, how='inner')
case4_data_uk_ftse.head()


# UK
weighted_return_c4_uk = (wts_c4 * case4_data_uk_ftse)

#Cumulative Portfolio UK
portfolio_return_c4_uk = weighted_return_c4_uk.sum(axis=1)
cumprod_portfolio_c4_uk = (1 + portfolio_return_c4_uk).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c4_uk, label='CASE 4(60E+40B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("UK Portfolio Performance C4")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case5_uk=selected_returns_dataframe(filtered_dates5)
case5_uk.head()


# Histogram of bond
case5_uk.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_uk=case5_uk.set_index('Date')
selected_returns_bond_uk.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case5_ftse=case5_ftse.set_index('Date')
case5_uk=case5_uk.set_index('Date')


case5_data_uk_ftse = case5_ftse.join(case5_uk, how='inner')
case5_data_uk_ftse.head()


# UK
weighted_return_c5_uk = (wts_c5 * case5_data_uk_ftse)

#Cumulative Portfolio UK
portfolio_return_c5_uk = weighted_return_c5_uk.sum(axis=1)
cumprod_portfolio_c5_uk = (1 + portfolio_return_c5_uk).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c5_uk, label='CASE 5(10E+90B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("UK Portfolio Performance C5")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


drawdown(case1_data_uk_ftse['Portfolio_Return_ftse']).min()

Wealth            811.673155
Previous Peak    1007.025383
Drawdown           -0.234502
dtype: float64


drawdown(case2_data_uk_ftse['Portfolio_Return_ftse']).min()

Wealth            970.312882
Previous Peak    1007.027930
Drawdown           -0.158305
dtype: float64


drawdown(case3_data_uk_ftse['Portfolio_Return_ftse']).min()

Wealth           1027.086708
Previous Peak    1027.086708
Drawdown           -0.176874
dtype: float64


drawdown(case4_data_uk_ftse['Portfolio_Return_ftse']).min()

Wealth           1033.644206
Previous Peak    1053.365340
Drawdown           -0.041933
dtype: float64


drawdown(case5_data_uk_ftse['Portfolio_Return_ftse']).min()

Wealth           878.165986
Previous Peak    940.885973
Drawdown          -0.160178
dtype: float64


drawdown(case1_data_uk_ftse['Portfolio_Return_uk_bond']).min()

Wealth            782.740247
Previous Peak    1014.896684
Drawdown           -0.526112
dtype: float64


drawdown(case2_data_uk_ftse['Portfolio_Return_uk_bond']).min()

Wealth            329.167905
Previous Peak    1014.860978
Drawdown           -0.771921
dtype: float64


drawdown(case3_data_uk_ftse['Portfolio_Return_uk_bond']).min()

Wealth           327.396370
Previous Peak    971.659919
Drawdown          -0.854435
dtype: float64


drawdown(case4_data_uk_ftse['Portfolio_Return_uk_bond']).min()

Wealth           1083.989501
Previous Peak    1083.989501
Drawdown           -0.132701
dtype: float64


drawdown(case5_data_uk_ftse['Portfolio_Return_uk_bond']).min()

Wealth            132.574669
Previous Peak    1126.213592
Drawdown           -0.958729
dtype: float64


a1_uk=case1_data_uk_ftse['Portfolio_Return_ftse'].sum()
a2_uk=case2_data_uk_ftse['Portfolio_Return_ftse'].sum()
a3_uk=case3_data_uk_ftse['Portfolio_Return_ftse'].sum()
a4_uk=case4_data_uk_ftse['Portfolio_Return_ftse'].sum()
a5_uk=case5_data_uk_ftse['Portfolio_Return_ftse'].sum()
a_uk=(a1_uk+a2_uk+a3_uk+a4_uk+a5_uk)

b1_uk=case1_data_uk_ftse['Portfolio_Return_uk_bond'].sum()
b2_uk=case2_data_uk_ftse['Portfolio_Return_uk_bond'].sum()
b3_uk=case3_data_uk_ftse['Portfolio_Return_uk_bond'].sum()
b4_uk=case4_data_uk_ftse['Portfolio_Return_uk_bond'].sum()
b5_uk=case5_data_uk_ftse['Portfolio_Return_uk_bond'].sum()
b_uk=(b1_uk+b2_uk+b3_uk+b4_uk+b5_uk)

c1_uk=filt_case1_ftse['^FTSE'].sum()
c2_uk=filt_case2_ftse['^FTSE'].sum()
c3_uk=filt_case3_ftse['^FTSE'].sum()
c4_uk=filt_case4_ftse['^FTSE'].sum()
c5_uk=filt_case5_ftse['^FTSE'].sum()
c_uk=(c1_uk+c2_uk+c3_uk+c4_uk+c5_uk)

d_uk=return_multiData['^FTSE'].sum()
final_uk=a_uk+b_uk+c_uk-d_uk
final_uk_usd=final_uk*1000000
final_uk_usd_per_change=((final_uk_usd-1000000)/1000000)*100
final_uk_usd_per_change

495.60029262693865


filtered_dates1 = ann_vol_portfolio[((ann_vol_portfolio['^GSPC'] < 15) & (ann_vol_portfolio['^GSPC'].diff() <10))]['DateTime']
filtered_dates4  = ann_vol_portfolio[(((27 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 25)) | ((15 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 10)))]['DateTime']
filtered_dates3  = ann_vol_portfolio[(((25 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 20)) | ((30 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 20)))]['DateTime']
filtered_dates2  = ann_vol_portfolio[(((20 > ann_vol_portfolio['^GSPC']) & (ann_vol_portfolio['^GSPC'] >= 15)) | ((20 > ann_vol_portfolio['^GSPC'].diff()) & (ann_vol_portfolio['^GSPC'].diff() > 15)))]['DateTime']
filtered_dates5  = ann_vol_portfolio[((ann_vol_portfolio['^GSPC']  >=  27) | (ann_vol_portfolio['^GSPC'].diff() >25))]['DateTime']


# Dates fullfilling the condition
def selected_returns_dataframe(filtered_dates):
  selected_returns = []
  for date in filtered_dates:
    return_value = df_bond_return.loc[df_bond_return['Date'] == date, 'US_Price'].values
    if len(return_value) > 0:
        selected_returns.append((date, return_value[0]))

  selected_return=pd.DataFrame(selected_returns)
  selected_return.set_axis(['Date', 'Portfolio_Return_us_bond'], axis=1, inplace=True)
  return selected_return


case1_us=selected_returns_dataframe(filtered_dates1)
case1_us.head()


# Histogram of bond
case1_us.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_us=case1_us.set_index('Date')
selected_returns_bond_us.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case1_gspc=case1_gspc.set_index('Date')
case1_us=case1_us.set_index('Date')


case1_data_us_gspc = case1_gspc.join(case1_us, how='inner')
case1_data_us_gspc.head()


# US
weighted_return_c1_us = (wts_c1 * case1_data_us_gspc)

#Cumulative Portfolio US
portfolio_return_c1_us = weighted_return_c1_us.sum(axis=1)
cumprod_portfolio_c1_us = (1 + portfolio_return_c1_us).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c1_us, label='CASE 1(90E+10B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("US Portfolio Performance C1")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case2_us=selected_returns_dataframe(filtered_dates2)
case2_us.head()


# Histogram of bond
case2_us.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_us=case2_us.set_index('Date')
selected_returns_bond_us.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case2_gspc=case2_gspc.set_index('Date')
case2_us=case2_us.set_index('Date')


case2_data_us_gspc = case2_gspc.join(case2_us, how='inner')
case2_data_us_gspc.head()


# US
weighted_return_c2_us = (wts_c2 * case2_data_us_gspc)

#Cumulative Portfolio US

portfolio_return_c2_us = weighted_return_c2_us.sum(axis=1)
cumprod_portfolio_c2_us = (1 + portfolio_return_c2_us).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c2_us, label='CASE 2(80E+20B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("US Portfolio Performance C2")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case3_us=selected_returns_dataframe(filtered_dates3)
case3_us.head()


# Histogram of bond
case3_us.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_us=case3_us.set_index('Date')
selected_returns_bond_us.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case3_gspc=case3_gspc.set_index('Date')
case3_us=case3_us.set_index('Date')


case3_data_us_gspc = case3_gspc.join(case3_us, how='inner')
case3_data_us_gspc.head()


# US
weighted_return_c3_us = (wts_c3 * case3_data_us_gspc)


#Cumulative Portfolio US
portfolio_return_c3_us = weighted_return_c3_us.sum(axis=1)
cumprod_portfolio_c3_us = (1 + portfolio_return_c3_us).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c3_us, label='CASE 3(70E+30B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("US Portfolio Performance C3")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case4_us=selected_returns_dataframe(filtered_dates4)
case4_us.head()


# Histogram of bond
case4_us.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_us=case4_us.set_index('Date')
selected_returns_bond_us.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case4_gspc=case4_gspc.set_index('Date')
case4_us=case4_us.set_index('Date')


case4_data_us_gspc = case4_gspc.join(case4_us, how='inner')
case4_data_us_gspc.head()


# US
weighted_return_c4_us = (wts_c4 * case4_data_us_gspc)

#Cumulative Portfolio US

portfolio_return_c4_us = weighted_return_c4_us.sum(axis=1)
cumprod_portfolio_c4_us = (1 + portfolio_return_c4_us).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c4_us, label='CASE 4(60E+40B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("US Portfolio Performance C4")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case5_us=selected_returns_dataframe(filtered_dates5)
case5_us.head()


# Histogram of bond
case5_us.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_us=case5_us.set_index('Date')
selected_returns_bond_us.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case5_gspc=case5_gspc.set_index('Date')
case5_us=case5_us.set_index('Date')


case5_data_us_gspc = case5_gspc.join(case5_us, how='inner')
case5_data_us_gspc.head()


# US
weighted_return_c5_us = (wts_c5 * case5_data_us_gspc)

#Cumulative Portfolio US
portfolio_return_c5_us = weighted_return_c5_us.sum(axis=1)
cumprod_portfolio_c5_us = (1 + portfolio_return_c5_us).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c5_us, label='CASE 5(10E+90B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("US Portfolio Performance C5")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


drawdown(case1_data_us_gspc['Portfolio_Return_gspc']).min()

Wealth            997.887166
Previous Peak    1001.507477
Drawdown           -0.264746
dtype: float64


drawdown(case2_data_us_gspc['Portfolio_Return_gspc']).min()

Wealth            934.543106
Previous Peak    1004.865097
Drawdown           -0.230831
dtype: float64


drawdown(case3_data_us_gspc['Portfolio_Return_gspc']).min()

Wealth           904.887319
Previous Peak    904.887319
Drawdown          -0.167142
dtype: float64


drawdown(case4_data_us_gspc['Portfolio_Return_gspc']).min()

Wealth           880.159448
Previous Peak    880.159448
Drawdown          -0.051831
dtype: float64


drawdown(case5_data_us_gspc['Portfolio_Return_gspc']).min()

Wealth           928.616787
Previous Peak    956.640488
Drawdown          -0.108736
dtype: float64


drawdown(case1_data_us_gspc['Portfolio_Return_us_bond']).min()

Wealth           700.869167
Previous Peak    987.041037
Drawdown          -0.463610
dtype: float64


drawdown(case2_data_us_gspc['Portfolio_Return_us_bond']).min()

Wealth            865.933146
Previous Peak    1001.052078
Drawdown           -0.532220
dtype: float64


drawdown(case3_data_us_gspc['Portfolio_Return_us_bond']).min()

Wealth           826.042726
Previous Peak    826.042726
Drawdown          -0.355127
dtype: float64


drawdown(case4_data_us_gspc['Portfolio_Return_us_bond']).min()

Wealth           625.528318
Previous Peak    692.235734
Drawdown          -0.096365
dtype: float64


drawdown(case5_data_us_gspc['Portfolio_Return_us_bond']).min()

Wealth            335.239196
Previous Peak    1159.895151
Drawdown           -0.773098
dtype: float64


a1_us=case1_data_us_gspc['Portfolio_Return_gspc'].sum()
a2_us=case2_data_us_gspc['Portfolio_Return_gspc'].sum()
a3_us=case3_data_us_gspc['Portfolio_Return_gspc'].sum()
a4_us=case4_data_us_gspc['Portfolio_Return_gspc'].sum()
a5_us=case5_data_us_gspc['Portfolio_Return_gspc'].sum()
a_us=(a1_us+a2_us+a3_us+a4_us+a5_us)

b1_us=case1_data_us_gspc['Portfolio_Return_us_bond'].sum()
b2_us=case2_data_us_gspc['Portfolio_Return_us_bond'].sum()
b3_us=case3_data_us_gspc['Portfolio_Return_us_bond'].sum()
b4_us=case4_data_us_gspc['Portfolio_Return_us_bond'].sum()
b5_us=case5_data_us_gspc['Portfolio_Return_us_bond'].sum()
b_us=(b1_us+b2_us+b3_us+b4_us+b5_us)

c1_us=filt_case1_gspc['^GSPC'].sum()
c2_us=filt_case2_gspc['^GSPC'].sum()
c3_us=filt_case3_gspc['^GSPC'].sum()
c4_us=filt_case4_gspc['^GSPC'].sum()
c5_us=filt_case5_gspc['^GSPC'].sum()
c_us=(c1_us+c2_us+c3_us+c4_us+c5_us)

d_us=return_multiData['^GSPC'].sum()
final_us=a_us+b_us+c_us-d_us
final_us_usd=final_us*1000000
final_us_usd_per_change=((final_us_usd-1000000)/1000000)*100
final_us_usd_per_change

570.4119952300248


filtered_dates1 = ann_vol_portfolio[((ann_vol_portfolio['^NSEI'] < 15) & (ann_vol_portfolio['^NSEI'].diff() <10))]['DateTime']
filtered_dates4  = ann_vol_portfolio[(((27 > ann_vol_portfolio['^NSEI']) & (ann_vol_portfolio['^NSEI'] >= 25)) | ((15 > ann_vol_portfolio['^NSEI'].diff()) & (ann_vol_portfolio['^NSEI'].diff() > 10)))]['DateTime']
filtered_dates3  = ann_vol_portfolio[(((25 > ann_vol_portfolio['^NSEI']) & (ann_vol_portfolio['^NSEI'] >= 20)) | ((30 > ann_vol_portfolio['^NSEI'].diff()) & (ann_vol_portfolio['^NSEI'].diff() > 20)))]['DateTime']
filtered_dates2  = ann_vol_portfolio[(((20 > ann_vol_portfolio['^NSEI']) & (ann_vol_portfolio['^NSEI'] >= 15)) | ((20 > ann_vol_portfolio['^NSEI'].diff()) & (ann_vol_portfolio['^NSEI'].diff() > 15)))]['DateTime']
filtered_dates5  = ann_vol_portfolio[((ann_vol_portfolio['^NSEI']  >=  27) | (ann_vol_portfolio['^NSEI'].diff() >25))]['DateTime']


# Dates fullfilling the condition
def selected_returns_dataframe(filtered_dates):
  selected_returns = []
  for date in filtered_dates:
    return_value = df_bond_return.loc[df_bond_return['Date'] == date, 'IN_Price'].values
    if len(return_value) > 0:
        selected_returns.append((date, return_value[0]))

  selected_return=pd.DataFrame(selected_returns)
  selected_return.set_axis(['Date', 'Portfolio_Return_IN_bond'], axis=1, inplace=True)
  return selected_return


case1_in=selected_returns_dataframe(filtered_dates1)
case1_in.head()


# Histogram of bond
case1_in.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_in=case1_in.set_index('Date')
selected_returns_bond_in.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case1_nsei=case1_nsei.set_index('Date')
case1_in=case1_in.set_index('Date')


case1_data_in_nsei = case1_nsei.join(case1_in, how='inner')
case1_data_in_nsei.head()


# India
weighted_return_c1_in = (wts_c1 * case1_data_in_nsei)

#Cumulative Portfolio in
portfolio_return_c1_in = weighted_return_c1_in.sum(axis=1)
cumprod_portfolio_c1_in = (1 + portfolio_return_c1_in).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c1_in, label='CASE 1(90E+10B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("India Portfolio Performance C1")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case2_in=selected_returns_dataframe(filtered_dates2)
case2_in.head()


# Histogram of bond
case2_in.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_in=case2_in.set_index('Date')
selected_returns_bond_in.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case2_nsei=case2_nsei.set_index('Date')
case2_in=case2_in.set_index('Date')


case2_data_in_nsei = case2_nsei.join(case2_in, how='inner')
case2_data_in_nsei.head()


# India
weighted_return_c2_in = (wts_c2 * case2_data_in_nsei)

#Cumulative Portfolio india
portfolio_return_c2_in = weighted_return_c2_in.sum(axis=1)
cumprod_portfolio_c2_in = (1 + portfolio_return_c2_in).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c2_in, label='CASE 2(80E+20B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("India Portfolio Performance C2")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case3_in=selected_returns_dataframe(filtered_dates3)
case3_in.head()


# Histogram of bond
case3_in.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_in=case3_in.set_index('Date')
selected_returns_bond_in.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case3_nsei=case3_nsei.set_index('Date')
case3_in=case3_in.set_index('Date')


case3_data_in_nsei = case3_nsei.join(case3_in, how='inner')
case3_data_in_nsei.head()


# India
weighted_return_c3_in = (wts_c3 * case3_data_in_nsei)

#Cumulative Portfolio india

portfolio_return_c3_in = weighted_return_c3_in.sum(axis=1)
cumprod_portfolio_c3_in = (1 + portfolio_return_c3_in).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c3_in, label='CASE 3(70E+30B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("India Portfolio Performance C3")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case4_in=selected_returns_dataframe(filtered_dates4)
case4_in.head()


# Histogram of bond
case4_in.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_in=case4_in.set_index('Date')
selected_returns_bond_in.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case4_nsei=case4_nsei.set_index('Date')
case4_in=case4_in.set_index('Date')


case4_data_in_nsei = case4_nsei.join(case4_in, how='inner')
case4_data_in_nsei.head()


# India
weighted_return_c4_in = (wts_c4 * case4_data_in_nsei)

#Cumulative Portfolio india
portfolio_return_c4_in = weighted_return_c4_in.sum(axis=1)
cumprod_portfolio_c4_in = (1 + portfolio_return_c4_in).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c4_in, label='CASE 4(60E+40B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("India Portfolio Performance C4")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case5_in=selected_returns_dataframe(filtered_dates5)
case5_in.head()


# Histogram of bond
case5_in.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_in=case5_in.set_index('Date')
selected_returns_bond_in.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case5_nsei=case5_nsei.set_index('Date')
case5_in=case5_in.set_index('Date')


case5_data_in_nsei = case5_nsei.join(case5_in, how='inner')
case5_data_in_nsei.head()


# India
weighted_return_c5_in = (wts_c5 * case5_data_in_nsei)

#Cumulative Portfolio india

portfolio_return_c5_in = weighted_return_c5_in.sum(axis=1)
cumprod_portfolio_c5_in = (1 + portfolio_return_c5_in).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c5_in, label='CASE 5(10E+90B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("India Portfolio Performance C5")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


drawdown(case1_data_in_nsei['Portfolio_Return_nsei']).min()

Wealth            957.939870
Previous Peak    1002.205158
Drawdown           -0.143928
dtype: float64


drawdown(case2_data_in_nsei['Portfolio_Return_nsei']).min()

Wealth            910.155246
Previous Peak    1001.106565
Drawdown           -0.206169
dtype: float64


drawdown(case3_data_in_nsei['Portfolio_Return_nsei']).min()

Wealth           830.063689
Previous Peak    923.878999
Drawdown          -0.101545
dtype: float64


drawdown(case4_data_in_nsei['Portfolio_Return_nsei']).min()

Wealth           870.195339
Previous Peak    870.195339
Drawdown           0.000000
dtype: float64


drawdown(case5_data_in_nsei['Portfolio_Return_nsei']).min()

Wealth            995.581791
Previous Peak    1038.904356
Drawdown           -0.085796
dtype: float64


drawdown(case1_data_in_nsei['Portfolio_Return_IN_bond']).min()

Wealth            926.035346
Previous Peak    1003.309635
Drawdown           -0.185690
dtype: float64


drawdown(case2_data_in_nsei['Portfolio_Return_IN_bond']).min()

Wealth            919.350124
Previous Peak    1003.797468
Drawdown           -0.238027
dtype: float64


drawdown(case3_data_in_nsei['Portfolio_Return_IN_bond']).min()

Wealth           968.486739
Previous Peak    990.901836
Drawdown          -0.022621
dtype: float64


drawdown(case4_data_in_nsei['Portfolio_Return_IN_bond']).min()

Wealth           1011.897208
Previous Peak    1011.897208
Drawdown            0.000000
dtype: float64


drawdown(case5_data_in_nsei['Portfolio_Return_IN_bond']).min()

Wealth            956.197194
Previous Peak    1013.183594
Drawdown           -0.084574
dtype: float64


a1_in=case1_data_in_nsei['Portfolio_Return_nsei'].sum()
a2_in=case2_data_in_nsei['Portfolio_Return_nsei'].sum()
a3_in=case3_data_in_nsei['Portfolio_Return_nsei'].sum()
a4_in=case4_data_in_nsei['Portfolio_Return_nsei'].sum()
a5_in=case5_data_in_nsei['Portfolio_Return_nsei'].sum()
a_in=(a1_in+a2_in+a3_in+a4_in+a5_in)

b1_in=case1_data_in_nsei['Portfolio_Return_IN_bond'].sum()
b2_in=case2_data_in_nsei['Portfolio_Return_IN_bond'].sum()
b3_in=case3_data_in_nsei['Portfolio_Return_IN_bond'].sum()
b4_in=case4_data_in_nsei['Portfolio_Return_IN_bond'].sum()
b5_in=case5_data_in_nsei['Portfolio_Return_IN_bond'].sum()
b_in=(b1_in+b2_in+b3_in+b4_in+b5_in)

c1_in=filt_case1_nsei['^NSEI'].sum()
c2_in=filt_case2_nsei['^NSEI'].sum()
c3_in=filt_case3_nsei['^NSEI'].sum()
c4_in=filt_case4_nsei['^NSEI'].sum()
c5_in=filt_case5_nsei['^NSEI'].sum()
c_in=(c1_in+c2_in+c3_in+c4_in+c5_in)

d_in=return_multiData['^NSEI'].sum()
final_in=a_in+b_in+c_in-d_in
final_in_usd=final_in*1000000
final_in_usd_per_change=((final_in_usd-1000000)/1000000)*100
final_in_usd_per_change

701.2344161308868


filtered_dates1 = ann_vol_portfolio[((ann_vol_portfolio['^HSI'] < 15) & (ann_vol_portfolio['^HSI'].diff() <10))]['DateTime']
filtered_dates4  = ann_vol_portfolio[(((27 > ann_vol_portfolio['^HSI']) & (ann_vol_portfolio['^HSI'] >= 25)) | ((15 > ann_vol_portfolio['^HSI'].diff()) & (ann_vol_portfolio['^HSI'].diff() > 10)))]['DateTime']
filtered_dates3  = ann_vol_portfolio[(((25 > ann_vol_portfolio['^HSI']) & (ann_vol_portfolio['^HSI'] >= 20)) | ((30 > ann_vol_portfolio['^HSI'].diff()) & (ann_vol_portfolio['^HSI'].diff() > 20)))]['DateTime']
filtered_dates2  = ann_vol_portfolio[(((20 > ann_vol_portfolio['^HSI']) & (ann_vol_portfolio['^HSI'] >= 15)) | ((20 > ann_vol_portfolio['^HSI'].diff()) & (ann_vol_portfolio['^HSI'].diff() > 15)))]['DateTime']
filtered_dates5  = ann_vol_portfolio[((ann_vol_portfolio['^HSI']  >=  27) | (ann_vol_portfolio['^HSI'].diff() >25))]['DateTime']


# Dates fullfilling the condition
def selected_returns_dataframe(filtered_dates):
  selected_returns = []
  for date in filtered_dates:
    return_value = df_bond_return.loc[df_bond_return['Date'] == date, 'CHI_Price'].values
    if len(return_value) > 0:
        selected_returns.append((date, return_value[0]))

  selected_return=pd.DataFrame(selected_returns)
  selected_return.set_axis(['Date', 'Portfolio_Return_chi_bond'], axis=1, inplace=True)
  return selected_return


case1_chi=selected_returns_dataframe(filtered_dates1)
case1_chi.head()


# Histogram of bond
case1_chi.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_chi=case1_chi.set_index('Date')
selected_returns_bond_chi.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case1_hsi=case1_hsi.set_index('Date')
case1_chi=case1_chi.set_index('Date')


case1_data_chi_hsi = case1_hsi.join(case1_chi, how='inner')
case1_data_chi_hsi.head()


# chi
weighted_return_c1_chi = (wts_c1 * case1_data_chi_hsi)

#Cumulative Portfolio chi
portfolio_return_c1_chi = weighted_return_c1_chi.sum(axis=1)
cumprod_portfolio_c1_chi = (1 + portfolio_return_c1_chi).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c1_chi, label='CASE 1(90E+10B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("china Portfolio Performance C1")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case2_chi=selected_returns_dataframe(filtered_dates2)
case2_chi.head()


# Histogram of bond
case2_chi.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_chi=case2_chi.set_index('Date')
selected_returns_bond_chi.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case2_hsi=case2_hsi.set_index('Date')
case2_chi=case2_chi.set_index('Date')


case2_data_chi_hsi = case2_hsi.join(case2_chi, how='inner')
case2_data_chi_hsi.head()


# china
weighted_return_c2_chi = (wts_c2 * case2_data_chi_hsi)

#Cumulative Portfolio chi
portfolio_return_c2_chi = weighted_return_c2_chi.sum(axis=1)
cumprod_portfolio_c2_chi = (1 + portfolio_return_c2_chi).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c2_chi, label='CASE 2(80E+20B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("china Portfolio Performance C2")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case3_chi=selected_returns_dataframe(filtered_dates3)
case3_chi.head()


# Histogram of bond
case3_chi.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_chi=case3_chi.set_index('Date')
selected_returns_bond_chi.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case3_hsi=case3_hsi.set_index('Date')
case3_chi=case3_chi.set_index('Date')


case3_data_chi_hsi = case3_hsi.join(case3_chi, how='inner')
case3_data_chi_hsi.head()


# china
weighted_return_c3_chi = (wts_c3 * case3_data_chi_hsi)

#Cumulative Portfolio chi

portfolio_return_c3_chi = weighted_return_c3_chi.sum(axis=1)
cumprod_portfolio_c3_chi = (1 + portfolio_return_c3_chi).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c3_chi, label='CASE 3(70E+30B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("china Portfolio Performance C3")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case4_chi=selected_returns_dataframe(filtered_dates4)
case4_chi.head()


# Histogram of bond
case4_chi.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_chi=case4_chi.set_index('Date')
selected_returns_bond_chi.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case4_hsi=case4_hsi.set_index('Date')
case4_chi=case4_chi.set_index('Date')


case4_data_chi_hsi = case4_hsi.join(case4_chi, how='inner')
case4_data_chi_hsi.head()


# china
weighted_return_c4_chi = (wts_c4 * case4_data_chi_hsi)

#Cumulative Portfolio chi
portfolio_return_c4_chi = weighted_return_c4_chi.sum(axis=1)
cumprod_portfolio_c4_chi = (1 + portfolio_return_c4_chi).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c4_chi, label='CASE 4(60E+40B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("china Portfolio Performance C4")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


case5_chi=selected_returns_dataframe(filtered_dates5)
case5_chi.head()


# Histogram of bond
case5_chi.plot.hist()

<Axes: ylabel='Frequency'>


selected_returns_bond_chi=case5_chi.set_index('Date')
selected_returns_bond_chi.plot()
plt.ylabel('Return')

Text(0, 0.5, 'Return')


case5_hsi=case5_hsi.set_index('Date')
case5_chi=case5_chi.set_index('Date')


case5_data_chi_hsi = case5_hsi.join(case5_chi, how='inner')
case5_data_chi_hsi.head()


# china
weighted_return_c5_chi = (wts_c5 * case5_data_chi_hsi)

#Cumulative Portfolio china
portfolio_return_c5_chi = weighted_return_c5_chi.sum(axis=1)
cumprod_portfolio_c5_chi = (1 + portfolio_return_c5_chi).cumprod()

#Plotting the solution portfolio's returns
plt.figure(figsize=(10, 5))
plt.plot(cumprod_portfolio_c5_chi, label='CASE 5(10E+90B)')
plt.legend(loc='upper left', fontsize=15)
plt.title("china Portfolio Performance C5")
plt.ylabel('Cumulative Return')

Text(0, 0.5, 'Cumulative Return')


drawdown(case1_data_chi_hsi['Portfolio_Return_hsi']).min()

Wealth           996.172675
Previous Peak    996.172675
Drawdown          -0.119870
dtype: float64


drawdown(case2_data_chi_hsi['Portfolio_Return_hsi']).min()

Wealth           584.939175
Previous Peak    997.114795
Drawdown          -0.485923
dtype: float64


drawdown(case3_data_chi_hsi['Portfolio_Return_hsi']).min()

Wealth            803.972373
Previous Peak    1041.030617
Drawdown           -0.330288
dtype: float64


drawdown(case4_data_chi_hsi['Portfolio_Return_hsi']).min()

Wealth           851.015016
Previous Peak    984.609360
Drawdown          -0.161397
dtype: float64


drawdown(case5_data_chi_hsi['Portfolio_Return_hsi']).min()

Wealth           744.164189
Previous Peak    974.637060
Drawdown          -0.314744
dtype: float64


drawdown(case1_data_chi_hsi['Portfolio_Return_chi_bond']).min()

Wealth            879.903384
Previous Peak    1006.814310
Drawdown           -0.144514
dtype: float64


drawdown(case2_data_chi_hsi['Portfolio_Return_chi_bond']).min()

Wealth            754.648085
Previous Peak    1005.571031
Drawdown           -0.329253
dtype: float64


drawdown(case3_data_chi_hsi['Portfolio_Return_chi_bond']).min()

Wealth           1003.850711
Previous Peak    1003.850711
Drawdown           -0.216480
dtype: float64


drawdown(case4_data_chi_hsi['Portfolio_Return_chi_bond']).min()

Wealth           926.063766
Previous Peak    999.334443
Drawdown          -0.073936
dtype: float64


drawdown(case5_data_chi_hsi['Portfolio_Return_chi_bond']).min()

Wealth           986.128217
Previous Peak    996.102055
Drawdown          -0.106969
dtype: float64


a1_chi=case1_data_chi_hsi['Portfolio_Return_hsi'].sum()
a2_chi=case2_data_chi_hsi['Portfolio_Return_hsi'].sum()
a3_chi=case3_data_chi_hsi['Portfolio_Return_hsi'].sum()
a4_chi=case4_data_chi_hsi['Portfolio_Return_hsi'].sum()
a5_chi=case5_data_chi_hsi['Portfolio_Return_hsi'].sum()
a_chi=(a1_chi+a2_chi+a3_chi+a4_chi+a5_chi)

b1_chi=case1_data_chi_hsi['Portfolio_Return_chi_bond'].sum()
b2_chi=case2_data_chi_hsi['Portfolio_Return_chi_bond'].sum()
b3_chi=case3_data_chi_hsi['Portfolio_Return_chi_bond'].sum()
b4_chi=case4_data_chi_hsi['Portfolio_Return_chi_bond'].sum()
b5_chi=case5_data_chi_hsi['Portfolio_Return_chi_bond'].sum()
b_chi=(b1_chi+b2_chi+b3_chi+b4_chi+b5_chi)

c1_chi=filt_case1_hsi['^HSI'].sum()
c2_chi=filt_case2_hsi['^HSI'].sum()
c3_chi=filt_case3_hsi['^HSI'].sum()
c4_chi=filt_case4_hsi['^HSI'].sum()
c5_chi=filt_case5_hsi['^HSI'].sum()
c_chi=(c1_chi+c2_chi+c3_chi+c4_chi+c5_chi)

d_chi=return_multiData['^HSI'].sum()
final_chi=a_chi+b_chi+c_chi-d_chi
final_chi_usd=final_chi*1000000
final_chi_usd_per_change=((final_chi_usd-1000000)/1000000)*100
final_chi_usd_per_change

144.09711313419874


import matplotlib.pyplot as plt
import numpy as np
data = {
    'Indices': ['^FTSE', '^GSPC', '^HSI', '^NSEI'],
    'USD_Value': [final_uk_usd, final_us_usd, final_chi_usd, final_in_usd],
    'Percentage_change':[final_uk_usd_per_change,final_us_usd_per_change,final_chi_usd_per_change,final_in_usd_per_change]
}

df=pd.DataFrame(data)
# Calculate the width of each bar
bar_width = 0.35

# Create a figure and primary y-axis (left)
fig, ax1 = plt.subplots()

# Create an array of x positions for the bars
x = np.arange(len(df['Indices']))

# Plot the first bar graph on the primary y-axis
ax1.bar(x - bar_width/2, df['USD_Value'], bar_width, color='orange', label='Return (US$ in million)')
ax1.set_xlabel('Indices')
ax1.set_ylabel('USD in million', color='orange')
ax1.tick_params(axis='y', labelcolor='orange')

# Create a secondary y-axis (right)
ax2 = ax1.twinx()

# Plot the second bar graph on the secondary y-axis
ax2.bar(x + bar_width/2, df['Percentage_change'], bar_width, color='blue', label='Percentage Change', alpha=0.7)
ax2.set_ylabel('% Change', color='blue')
ax2.tick_params(axis='y', labelcolor='blue')

# Adjust x-axis positions and labels
ax1.set_xticks(x)
ax1.set_xticklabels(df['Indices'])

# Combine legends from both axes
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
lines = lines1 + lines2
labels = labels1 + labels2

# Display a single legend for both bar graphs
ax1.legend(lines, labels, loc='upper right')

plt.title('Net Return of Volatility Switched Portfolio over Buy and Hold Strategy and % Change of USD 1 Million Investment')
plt.tight_layout()
plt.show()


Tickers_P = [ '^FTSE', '^GSPC', '^HSI','^NSEI','GLD']
Start_Date1 = "2020-01-01"
End_Date1 = "2020-12-31"


CovidData = GetAdjCloseQuotes(Tickers_P, Start_Date1, End_Date1)
CovidData.head()


# Calculating daily returns
return_CovidData = CovidData.pct_change().dropna()
return_CovidData


# Cumulative return
cumprod_return_CovidData = (1 + return_CovidData).cumprod()
cumprod_return_CovidData.head()


fig, axes = plt.subplots(1, 2, sharex=True, figsize=(20,5))

sns.lineplot(data=return_CovidData, dashes=False, ax=axes[0])
axes[0].set_ylabel('Return')
axes[0].set_title('Return of Equity During Covid')


sns.lineplot(data=cumprod_return_CovidData, dashes=False, ax=axes[1])
axes[1].set_ylabel('Cumulative return')
axes[1].set_title('Cumulative Returns of Equity During Covid')

Text(0.5, 1.0, 'Cumulative Returns of Equity During Covid')


bond_CovidData=df_bond.loc[Start_Date1:End_Date1]
bond_CovidData.head()


# Return
bond_return_CovidData=bond_CovidData.pct_change().dropna()
bond_return_CovidData.head()


# Cumulative Return
cumprod_cum_return_CovidData=(1+bond_return_CovidData).cumprod()
cumprod_cum_return_CovidData.head()


fig, axes = plt.subplots(1, 2, sharex=True, figsize=(20,5))

sns.lineplot(data=bond_return_CovidData, dashes=False, ax=axes[0])
axes[0].set_ylabel('Return')
axes[0].set_title('Return of Bond During Covid')


sns.lineplot(data=cumprod_cum_return_CovidData, dashes=False, ax=axes[1])
axes[1].set_ylabel('Cumulative return')
axes[1].set_title('Cumulative Returns of Bond During Covid')

Text(0.5, 1.0, 'Cumulative Returns of Bond During Covid')

	^FTSE	^GSPC	^HSI	^NSEI	GLD	GOLD.AX
DateTime
2009-01-02	4561.799805	931.799988	15042.809570	3046.750000	86.230003	124.330002
2009-01-05	4579.600098	927.450012	15563.309570	3121.449951	84.480003	120.639999
2009-01-06	4638.899902	934.700012	15509.509766	3112.800049	85.129997	118.199997
2009-01-07	4507.500000	906.650024	14987.459961	2920.399902	82.750000	117.660004
2009-01-09	4448.500000	890.349976	14377.440430	2873.000000	83.919998	119.599998

	^FTSE	^GSPC	^HSI	^NSEI	GLD	GOLD.AX
DateTime
2009-01-05	0.003902	-0.004668	0.034601	0.024518	-0.020295	-0.029679
2009-01-06	0.012949	0.007817	-0.003457	-0.002771	0.007694	-0.020225
2009-01-07	-0.028326	-0.030010	-0.033660	-0.061809	-0.027957	-0.004568
2009-01-09	-0.013089	-0.017978	-0.040702	-0.016231	0.014139	0.016488
2009-01-12	-0.005013	-0.022564	-0.028269	-0.034772	-0.037655	0.011957
...	...	...	...	...	...	...
2023-04-21	0.001455	0.000903	-0.015749	-0.000023	-0.009888	0.002906
2023-04-24	-0.000240	0.000852	-0.005768	0.006772	0.003039	-0.004346
2023-04-26	-0.007533	-0.019591	-0.010154	0.003956	-0.000379	0.018188
2023-04-27	-0.002674	0.019566	0.004201	0.005695	0.000054	-0.001072
2023-04-28	0.004980	0.008253	0.002736	0.008370	0.000271	-0.005722

	^FTSE	^GSPC	^HSI	^NSEI	GLD	GOLD.AX
DateTime
2010-02-10	0.232625	0.277415	0.335965	0.348596	0.215220	0.232228
2010-02-11	0.232659	0.277507	0.334748	0.348095	0.214936	0.230634
2010-02-17	0.233344	0.278052	0.334885	0.348433	0.215652	0.230759
2010-02-18	0.231682	0.276385	0.333109	0.342569	0.213742	0.230807
2010-02-19	0.231327	0.275734	0.331550	0.342246	0.213427	0.230304

	^FTSE	^GSPC	^HSI	^NSEI	GLD	GOLD.AX
DateTime
2023-04-21	0.001478	0.002264	0.000561	0.003981	0.001780	0.000194
2023-04-24	0.001499	0.002264	0.000561	0.003982	0.001780	0.000194
2023-04-26	0.001537	0.002258	0.000563	0.004000	0.001789	0.000193
2023-04-27	0.001537	0.002267	0.000567	0.004045	0.001796	0.000194
2023-04-28	0.001536	0.002267	0.000567	0.004047	0.001822	0.000195

	^FTSE	^GSPC	^HSI	^NSEI	GLD	GOLD.AX
DateTime
2009-01-05	1.003902	0.995332	1.034601	1.024518	0.979705	0.970321
2009-01-06	1.016901	1.003112	1.031025	1.021679	0.987243	0.950696
2009-01-07	0.988097	0.973009	0.996321	0.958530	0.959643	0.946352
2009-01-09	0.975163	0.955516	0.955768	0.942972	0.973211	0.961956
2009-01-12	0.970275	0.933956	0.928749	0.910183	0.936565	0.973458

	^FTSE	^GSPC	^HSI	^NSEI	GLD	GOLD.AX
DateTime
2010-02-10	23.0	28.0	34.0	35.0	22.0	23.0
2010-02-11	23.0	28.0	33.0	35.0	21.0	23.0
2010-02-17	23.0	28.0	33.0	35.0	22.0	23.0
2010-02-18	23.0	28.0	33.0	34.0	21.0	23.0
2010-02-19	23.0	28.0	33.0	34.0	21.0	23.0

	ftse_group	gspc_group	hsi_group	nsei_group	gld_group	gold.ax_group
DateTime
2023-04-21	10-19	20-29	30-39	10-19	10-19	10-19
2023-04-24	10-19	20-29	30-39	10-19	10-19	10-19
2023-04-26	10-19	20-29	30-39	10-19	10-19	10-19
2023-04-27	10-19	20-29	30-39	10-19	10-19	10-19
2023-04-28	10-19	20-29	30-39	10-19	10-19	10-19

	Date	Portfolio_Return_ftse
0	2013-02-22	0.007025
1	2013-02-25	0.003109
2	2013-03-01	0.002798
3	2013-03-04	-0.005174
4	2013-03-06	-0.000684

	Date	Portfolio_Return_ftse
0	2010-04-20	0.009742
1	2010-04-21	-0.010426
2	2010-04-22	-0.010151
3	2010-04-23	0.010308
4	2010-04-27	-0.021000

	Date	Portfolio_Return_ftse
0	2010-02-10	0.003952
1	2010-02-11	0.005748
2	2010-02-17	0.022300
3	2010-02-18	0.009192
4	2010-02-19	0.006216

	Date	Portfolio_Return_ftse
0	2020-04-07	0.053365
1	2020-04-08	-0.004698
2	2020-04-09	0.029061
3	2020-04-15	-0.041933
4	2020-04-16	0.005484

	Date	Portfolio_Return_ftse
0	2020-05-04	-0.059114
1	2020-05-05	0.016615
2	2020-05-06	0.000752
3	2020-05-07	0.014042
4	2020-05-11	0.000623

	Date	Portfolio_Return_gspc
0	2013-01-23	0.001507
1	2013-01-24	0.000007
2	2013-01-25	0.005445
3	2013-01-29	0.003247
4	2013-01-30	-0.003900

	Date	Portfolio_Return_gspc
0	2010-06-23	-0.002985
1	2010-06-28	-0.002034
2	2010-07-02	-0.007888
3	2010-07-06	0.005359
4	2010-07-07	0.031331

	Date	Portfolio_Return_gspc
0	2010-04-09	0.006684
1	2010-04-12	0.001767
2	2010-04-13	0.000685
3	2010-04-15	0.012002
4	2010-04-16	-0.016127

	Date	Portfolio_Return_gspc
0	2010-03-03	0.000429
1	2010-03-04	0.003736
2	2010-03-05	0.014007
3	2010-03-08	-0.000176
4	2010-03-09	0.001713

	Date	Portfolio_Return_hsi
0	2015-03-20	-0.003827
1	2015-03-23	0.004893
2	2015-03-24	-0.003875
3	2015-03-25	0.005272
4	2015-03-26	-0.001270

	Date	Portfolio_Return_gspc
0	2010-02-10	-0.002233
1	2010-02-11	0.009680
2	2010-02-17	0.019509
3	2010-02-18	0.006585
4	2010-02-19	0.002187

	Date	Portfolio_Return_hsi
0	2010-12-31	0.001570
1	2011-01-04	0.027481
2	2011-01-05	0.003775
3	2011-01-06	0.001199
4	2011-01-07	-0.004190

	Date	Portfolio_Return_hsi
0	2010-07-07	-0.011305
1	2010-07-08	0.009744
2	2010-07-09	0.016364
3	2010-07-12	0.004356
4	2010-07-13	-0.001777

	Date	Portfolio_Return_hsi
0	2010-06-07	-0.020319
1	2010-06-08	0.005642
2	2010-06-09	0.006864
3	2010-06-10	0.000584
4	2010-06-11	0.012208

	Date	Portfolio_Return_hsi
0	2010-02-10	0.006667
1	2010-02-11	0.018495
2	2010-02-17	0.011992
3	2010-02-18	-0.005448
4	2010-02-19	-0.025861

	Date	Portfolio_Return_nsei
0	2014-11-11	0.002205
1	2014-11-12	0.002469
2	2014-11-13	-0.003036
3	2014-11-14	0.003835
4	2014-11-17	0.004869

	Date	Portfolio_Return_nsei
0	2010-08-20	-0.001724
1	2010-08-23	0.002323
2	2010-08-24	-0.006927
3	2010-08-25	-0.007766
4	2010-08-26	0.002847

	Date	Portfolio_Return_nsei
0	2010-06-23	0.001241
1	2010-06-24	-0.000479
2	2010-06-25	-0.009689
3	2010-06-28	0.012232
4	2010-06-29	-0.014503

	Date	Portfolio_Return_nsei
0	2010-02-10	-0.007397
1	2010-02-11	0.014641
2	2010-02-17	0.018055
3	2010-02-18	-0.005342
4	2010-02-19	-0.008767

	Date	Portfolio_Return_gld
0	2013-01-23	-0.002810
1	2013-01-24	-0.010968
2	2013-01-25	-0.004770
3	2013-01-29	0.002116
4	2013-01-30	0.007454

Adding libraries¶

EQUITY¶

Testing volatility Scenarios¶

For ^FTSE¶

CASE 1¶

CASE 2¶

CASE 3¶

CASE 4¶

CASE 5¶

FOR ^GSPC¶

CASE 1¶

CASE 2¶

CASE 3¶

CASE 4¶

CASE 5¶

FOR ^HSI¶

CASE 1¶

CASE 2¶

CASE 3¶

CASE 4¶

CASE 5¶

FOR ^NSEI¶

CASE 1¶

CASE 2¶

CASE 3¶

CASE 4¶

CASE 5¶

FOR GLD¶

CASE 1¶

CASE 2¶

CASE 3¶

CASE 4¶

CASE 5¶

BOND DATA¶

FOR UK BOND¶

CASE 1¶

CASE 2¶

CASE 3¶

CASE 4¶

CASE 5¶

FOR UNITED STATES¶

CASE 1¶

CASE 2¶

CASE 3¶

CASE 4¶

CASE 5¶

FOR INDIA¶

CASE 1¶

CASE 2¶

CASE 3¶

CASE 4¶

CASE 5¶

FOR CHINA¶

CASE 1¶

CASE 2¶

CASE 3¶

CASE 4¶

CASE 5¶

APPENDIX: COVID-19 ANALYSIS SUMMARY¶

	Date	Portfolio_Return_gld
0	2010-06-23	-0.004117
1	2010-06-28	-0.013604
2	2010-07-02	-0.026216
3	2010-07-06	-0.016710
4	2010-07-07	0.010471

	Date	Portfolio_Return_gld
0	2010-04-09	0.008788
1	2010-04-12	-0.005544
2	2010-04-13	-0.002832
3	2010-04-15	0.008519
4	2010-04-16	-0.021205

	Date	Portfolio_Return_gld
0	2010-03-03	0.005495
1	2010-03-04	-0.007166
2	2010-03-05	-0.000180
3	2010-03-08	-0.008393
4	2010-03-09	-0.001456

	Date	Portfolio_Return_gld
0	2010-02-10	-0.002751
1	2010-02-11	0.019121
2	2010-02-17	0.019789
3	2010-02-18	0.006682
4	2010-02-19	-0.004637

	US_Price	IN_Price	UK_Price	CHI_Price
Date
01/05/2023	3.722	7.327	3.552	2.870
01/04/2023	3.690	7.321	3.493	2.863
01/03/2023	3.752	7.321	3.653	2.873
01/02/2023	3.879	7.340	3.699	NaN
12/30/2022	3.879	7.327	3.669	2.884

	US_Price	IN_Price	UK_Price	CHI_Price
Date
2023-01-05	3.722	7.327	3.552	2.870
2023-01-04	3.690	7.321	3.493	2.863
2023-01-03	3.752	7.321	3.653	2.873
2023-01-02	3.879	7.340	3.699	NaN
2022-12-30	3.879	7.327	3.669	2.884
...	...	...	...	...
2013-01-08	1.864	7.906	2.030	3.600
2013-01-07	1.901	7.900	2.086	3.590
2013-01-04	1.903	7.930	2.117	3.610
2013-01-03	1.911	7.967	2.073	NaN
2013-01-02	1.835	7.993	1.988	NaN

	US_Price	IN_Price	UK_Price	CHI_Price
Date
2023-01-05	0.000000	0.000000	0.000000	0.000000
2023-01-04	-0.008598	-0.000819	-0.016610	-0.002439
2023-01-03	0.016802	0.000000	0.045806	0.003493
2022-12-30	0.033849	0.000820	0.004380	0.003829
2022-12-29	-0.015210	-0.002184	-0.002180	-0.000693

	Date	Portfolio_Return_uk_bond
0	2013-02-22	0.014897
1	2013-02-25	0.042585
2	2013-03-01	-0.019916
3	2013-03-04	-0.027523
4	2013-03-06	-0.028798

	Date	Portfolio_Return_uk_bond
0	2013-01-04	0.014861
1	2013-01-07	0.027586
2	2013-01-08	0.002964
3	2013-01-09	-0.034351
4	2013-01-10	0.006241

	Portfolio_Return_ftse	Portfolio_Return_uk_bond
Date
2013-01-04	0.007028	0.014861
2013-01-07	-0.004138	0.027586
2013-01-08	-0.001814	0.002964
2013-01-09	0.007450	-0.034351
2013-01-10	0.000459	0.006241